Description:TECHNICAL FIELD
[0001] The embodiments of the present disclosure generally relate to communication systems. In particular, the present disclosure relates to a sensing system and method for characterization of objects using a beamforming-based Integrated Sensing and Communication (ISAC) system.

BACKGROUND
[0002] As a demand for sensing capabilities grows in fifth generation (5G) and beyond, it has become essential to optimize spectrum efficiency and energy efficiency while introducing sensing related capabilities along with communication. To achieve effective introduction of sensing capabilities along with the existing communication capabilities of a base station (BS) or gNodeB, the BS is required to act as a collocated, monostatic, and active sensing system.
[0003] Enhancing the sensing range at higher frequencies in 5G and beyond poses significant challenges, especially when it comes to effectively filtering out clutter from detected targets. Using 5G New Radio (NR) Synchronization Signal Block (SSB) signals for sensing also face limitations, such as restricted coverage range, low resolution, adaptively tuning the sensing signal based on varying environmental conditions. Further, existing technology also poses challenges in accurate clutter suppression while staying within system power constraints and without affecting communication performance.
[0004] Although the use of the 5G NR SSB signals for sensing is already established, it gives only the range of potential target object. However, accurate identification of target positioning is still evolving. Additionally, identifying objects, detecting multiple target contours, and filtering out clutter is still challenging while achieving extended sensing range. This highlights a need for advanced Integrated Sensing and Communication (ISAC) solutions in 5G and beyond to overcome these challenges to fully realize the potential of ISAC systems.
[0005] Further, existing solutions do not adequately provide an integrated approach to improve sensing range and accuracy. Additionally, the issue of clutter suppression in complex environments remains unaddressed, and the existing solutions fail to implement advanced filtering techniques. Furthermore, the existing solutions lack an adaptive sensing mechanism capable of dynamically adjusting sensing beams to optimize performance across different environmental conditions.
[0006] Therefore, there is, a need for a system and method that seamlessly combines communication and sensing functionalities, leveraging 5G New Radio (NR) signals to perform both tasks efficiently without compromising system performance.

OBJECTS OF THE PRESENT DISCLOSURE
[0007] An object of the present disclosure is to provide a system and a method for improving sensing range and accuracy by utilizing both azimuth and elevation planes for object detection, identification, and characterization.
[0008] Another object of the present disclosure is to provide a system and a method that leverages 5th Generation (5G) New Radio (NR) Synchronization Signal Block (SSB) signals for Integrated Sensing and Communication (ISAC) system or joint Communication and Sensing (JCAS) system to enhance sensing range while addressing power constraints.
[0009] Another object of the present disclosure is to provide a system and a method that transmits sensing signals comprising a synchronization signal block (SSB) beam in an azimuthal plane and an elevation plane, and captures reflected sensing signals corresponding to the transmitted sensing signals.
[0010] Another object of the present disclosure is to provide a system and a method that filters effective sensing signals from the reflected sensing signals based on a threshold value.
[0011] Another object of the present disclosure is to provide a system and a method that retrieves azimuthal scan sensing information and elevation scan sensing information from correlation between the transmitted sensing signals and the effective sensing signals.
[0012] Another object of the present disclosure is to provide a system and a method that iteratively integrates the azimuthal scan sensing information and the elevation scan sensing information obtained by performing D-number of azimuthal and elevation scans to estimate an Angle of Arrival (AoA) and a range of objects.
[0013] Another object of the present disclosure is to provide a system and a method that processes the integrated data to identify signatures representing distinctive characteristics of the objects, and generate a signature grid from the identified signatures.
[0014] Another object of the present disclosure is to provide a system and a method that identifies patterns of the objects by analysing spatial and temporal relationships within the signature grid.

SUMMARY
[0015] This section is provided to introduce certain objects and aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.
[0016] In an aspect, the present disclosure relates to a method for characterization of one or more objects using a beamforming-based Integrated Sensing and Communication (ISAC) system. The method includes transmitting, by a sensing system, sensing signals comprising a synchronization signal block (SSB) beam in an azimuthal plane and an elevation plane. The method includes capturing, by the sensing system, reflected sensing signals corresponding to the transmitted sensing signals. The method includes filtering, by the sensing system, effective sensing signals from the reflected sensing signals based on a threshold value. The method includes, upon retrieving the effective sensing signals corresponding to the azimuthal plane and the elevation plane, correlating, by the sensing system, the effective sensing signals with the transmitted sensing signals for each delay block for a D-number of azimuthal scan and a D-number of elevation scan, where D pertains to a number of delay blocks in the sensing system.
[0017] Further, the method includes retrieving, by the sensing system, azimuthal scan sensing information and elevation scan sensing information from correlation between the transmitted sensing signals and the effective sensing signals, during the azimuthal scan and the elevation scan, respectively. The method includes iteratively integrating, by the sensing system, the azimuthal scan sensing information and the elevation scan sensing information obtained by performing D-number of azimuthal and elevation scans to estimate an Angle of Arrival (AoA) and a range of the one or more objects. The method includes processing, by the sensing system, the integrated data to identify signatures representing distinctive characteristics of the one or more objects. The method includes generating, by the sensing system, a signature grid from the identified signatures. The method includes identifying, by the sensing system, patterns of the one or more objects by analysing spatial and temporal relationships within the signature grid.

BRIEF DESCRIPTION OF DRAWINGS
[0018] The accompanying drawings, which are incorporated herein, and constitute a part of this invention, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings.
[0019] FIGs. 1A and 1B illustrate example system architectures (100A, 100B) of a sensing system (102) for scanning and detection of objects, respectively in accordance with an embodiment of the present disclosure.
[0020] FIG. 2 illustrates an example flow diagram of a method (200) implemented by the sensing system (102) for scanning of objects, in accordance with an embodiment of the present disclosure.
[0021] FIG. 3 illustrates a schematic diagram (300) of transmission of a sensing signal beam by the sensing system (102) for scanning of objects, in accordance with an embodiment of the present disclosure.
[0022] FIG. 4 illustrates an example flow diagram (400) of a method implemented by the sensing system (102) for calculating an Angle of Arrival (AoA) of objects, in accordance with an embodiment of the present disclosure.
[0023] FIG. 5 illustrates an example flow diagram (500) of a method implemented by the sensing system (102) for target/object detection, in accordance with an embodiment of the present disclosure.
[0024] FIG. 6 illustrates a schematic diagram (600) of horizontal plane scanning by the sensing system (102), in accordance with an embodiment of the present disclosure.
[0025] FIGs. 7A-7D illustrate example graphical plots (700A, 700B, 700C, 700D) representing phase-delay profiles, in accordance with embodiments of the present disclosure.
[0026] The foregoing shall be more apparent from the following more detailed description of the invention.

DETAILED DESCRIPTION OF INVENTION
[0027] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.
[0028] The present disclosure leverages Fifth Generation (5G) New Radio (NR) Synchronization Signal Block (SSB) signals, comprising Primary Synchronizing Signal (PSS) and Secondary Synchronizing Signal (SSS), for Integrated Sensing and Communication (ISAC) to enhance sensing range while addressing power constraints. Operating in a monostatic configuration, the sensing system (gNodeB) uses Transmitting (Tx) and Receiving (Rx) beamforming modules at the same location, to form beams. The sensing system transmits PSS/SSS beams as sensing signal beam in an azimuth plane (horizontal plane) varying azimuthal angle ( ) = 0° to 360°, keeping elevation angle (θ) = constant, and in an elevation plane (vertical plane) varying elevation angle (θ) = 0° to 180°, keeping azimuthal angle ( ) = constant. Further, the sensing system extracts and correlates received signals to identify objects and environmental conditions. To cater the effects of overlapping reflected sensing signals received from multiple paths (because of Line-of-Sight and Non- Line-of-Sight components), the sensing system identifies accurate angle-of-arrival (AoA) or direction-of-arrival (DoA) and hence more accurate positioning of the object (to be identified).
[0029] Further, the sensing system generates a signature grid by integrating azimuth and elevation scans, capturing range, angles, and correlation strengths of transmitted and received sensing signal. Artificial Intelligence (AI)-based pattern recognition identifies environmental patterns and flags anomalies. The sensing system also detects target and filters clutter by analysing phase-delay profile to identify multiple terrestrial terrain target contours. If additional sensing range is needed, higher layers may allocate more Physical Resource Elements (PREs) for enhanced sensing beams.
[0030] Various embodiments of the present disclosure will be explained in detail with reference to FIGs. 1A-7D.
[0031] FIG. 1A illustrates an example system architecture (100A) of a sensing system (102) for scanning of objects, in accordance with an embodiment of the present disclosure. The sensing system (102) performs characterization of one or more objects, which includes detection and identification of the one or more objects, specifically a location of the one or more objects. Further, the characterization may also refer to physical characteristics (e.g., size, shape, material properties) and spatial attributes (e.g., location, position, distance, angle) of the objects.
[0032] Referring to FIG. 1A, the sensing system (102) may function as a monostatic and active sensing system, such as a base station or a gNodeB. In such a configuration, the transmitting (Tx) and receiving (Rx) antennas may be located at the same position. A waveform generator (104) may be utilized to create and evaluate waveforms used in 5G New Radio (NR) communication systems. The waveform generator (104) plays a crucial role in the development, testing, and validation of 5G technology by ensuring compliance with standards and optimizing performance. The waveform generator (104) produces a range of waveforms required for 5G NR, including physical layer signals and channels as specified by standards such as the Third Generation Partnership Project (3GPP). These waveforms support both uplink and downlink communications. The waveform generator (104) specifically produces a 5G NR Synchronizing Signal Block (SSB) signal and/or Physical Downlink Shared Channel (PDSCH).
[0033] In an embodiment, a Boolean logic (106) may be utilized by the sensing system (102) to ensure that the transmitted PSS, SSS, or PDSCH signals correlate with their corresponding reflected signals (from a stationary terrestrial terrain target) over two alternate Orthogonal Frequency Division Multiplexing (OFDM) symbol durations for PSS and SSS, and one symbol duration for PDSCH, while filtering out other 5G NR signals. When the transmitter (Tx) block (108) in the sensing system (102) sends 5G NR PSS, SSS, or PDSCH signal beam as the sensing signal beam, the Boolean logic (106) becomes true (i.e., "YES" or "logic 1"), allowing the transmitted 5G NR PSS/ SSS/ PDSCH signal to pass to a correlator (126). Further, when the transmitter (Tx) block (108) in the sensing system (102) sends any signal other than the 5G NR PSS, SSS, or PDSCH signal beam, the Boolean logic (106) becomes false (i.e., "NO" or "logic 0"), blocking the other signals to pass to the correlator (126). Further, delay blocks (110) may be configured between the Boolean logic (106) and a sensing module (124).
[0034] In an embodiment, the transmitter (Tx) block (108) may be operatively connected to a beamforming controller-1 (112) and a beamforming controller-2 (114). The beamforming controller-1 (112) and the beamforming controller-2 (114) may be operatively connected to a transmitter beamforming module (116) through a binary switch (120). The binary switch (120) may perform switching between the beamforming controller-1 (112) and the beamforming controller-2 (114) during transmission of the sensing signals. The binary switch (120) may select between azimuthal and elevation steering by choosing either the beamforming controller-1 (112) or the beamforming controller-2 (114).
[0035] The transmitter beamforming module (116) within the sensing system (102) may be responsible for implementing beamforming during signal transmission. The primary function of the transmitter beamforming module (116) may be to direct radio frequency (RF) signals toward a specific target, enhancing signal strength and reducing interference. This precise approach improves data rates, coverage, and overall network performance. Implementing transmitter beamforming module (116) requires precise control over multiple antennas (antenna arrays), often arranged in a phased array configuration. By adjusting the phase and amplitude of each antenna element, the sensing system (102) can steer beams electronically without mechanical movement. This electronic steering allows for rapid beam direction changes, essential for tracking mobile users or targets.
[0036] In an embodiment, the transmitter block (108) may transmit the PSS/SSS sensing signals to the beamforming controller-1 (112) and the beamforming controller-2 (114). The binary switch (120) may select between azimuthal and elevation steering by choosing either the beamforming controller-1 (112) or the beamforming controller-2 (114). Further, the PSS/SSS sensing signals may be transmitted to the transmitter beamforming module (116). The transmitter beamforming module (116) may continuously transmit the PSS/SSS sensing signal beams (134). The transmitted PSS/SSS sensing signal beams (134) may be reflected back when there are any objects (138). The present disclosure is aimed to estimate the distance between the object (138) and the sensing system (102), and determine the direction and/or position of the objects (138) in terms of azimuth and elevation angles.
[0037] In an embodiment, a receiving beamforming module (118) may receive a reflected noisy signal beam (136). Further, the receiving beamforming module (118) may transmit the reflected noisy signal beam (136) to a receiving block (120), which may perform noise filtering (122) and reception and processing (124) of the reflected noisy signal beam (136).
[0038] In an embodiment, the delay blocks (110) may be integral components in the sensing system (102), introducing processing delays to ensure that the transmitted PSS/SSS sensing signal beams (134) align with the received PSS/SSS signal beams (136) after reflecting from the objects (138). The number of delay blocks, denoted as D, may be determined by required iterations of horizontal and vertical scanning cycles. These iterations are essential for precise objects/targets detection and effective clutter elimination. Each delay operates sequentially, contributing to the system's overall performance. The number of scanning cycles may be decided based on the requirements. By increasing the scanning cycles, scanning precision may be increased.
[0039] In an embodiment, the receiving beamforming module (118) may be connected to the sensing module (124). The sensing module (124) may include a correlator (126), a storage and fusion block (128), and a decision block (130). The correlator (126) may measure a similarity between transmitted and received signal beams by performing cross-correlation, estimating a time delay essential for accurate target localization by comparing reflected signals (like PSS, SSS, or PDSCH) with stored references. The storage and fusion block (128) may collect the correlator outputs during sequential horizontal (azimuth) and vertical (elevation) scans performed by two beamforming controllers (112, 114), storing the results from three cycles each and then fusing the datasets to create integrated sensing information. Further, the decision block (130) may process the fused data using Artificial Intelligence (AI)-driven pattern recognition techniques on signature grids to identify environmental patterns and promptly flag any anomalies, such as unexpected reflections or sudden changes, for further analysis. The sensing module (124) may record the processed information in a database (132).
[0040] In response to processing the integrated data, the sensing system (102) may continuously analyse, using the AI technique, the identified signatures, detect a deviation in at least one signature among the identified signatures, and promptly transmit an anomaly alert to an operator of the sensing system (102) based on the detection.
[0041] In an embodiment, upon generating the signature grid, the sensing system (102) may compare the generated signature grid with a pre-existing signature grid, and predict, using the AI technique, an entry of new objects into the environment based on a difference identified during the comparison. Further, the sensing system (102) may update the generated signature grid based on the prediction. Further, the sensing system (102) may also update the generated signature grid and classify the objects in the environments as temporary objects or permanent objects. In addition, based on the variation of the generated signature grid from previously stored signature grids at different time intervals, the sensing system (102) may identify the progress/changes in the environment, for example, but not limited to, construction of building, etc. Depending on the requirement, the time period for updating the signature grid may vary.
[0042] In an embodiment, the sensing system (102) may continuously detect, using the AI technique, a strength or a sensing range of the effective sensing signals, and transmitting a control signal to a higher communication layer, requesting allocation of additional Resource Elements (REs) for generating enhanced sensing beams when the strength or the sensing range of the effective sensing signals is less than a predetermined threshold value.
[0043] In an embodiment, the sensing system (102) may transmit the sensing signals (134) including the SSB beam in an azimuthal plane and an elevation plane. The sensing system (102) may capture reflected sensing signals (136) corresponding to the transmitted sensing signals using the receiving beamforming module (118). The sensing system (102) may filter effective sensing signals from the reflected sensing signals based on a threshold value, using the noise filtering (122). Upon retrieving the effective sensing signals corresponding to the azimuthal plane and the elevation plane, the sensing system (102) may correlate, using the correlator (126), the effective sensing signals with the transmitted sensing signals for each delay block (110) for a D-number of azimuthal scan and a D-number of elevation scan, wherein D pertains to a number of delay blocks in the sensing system (102). The sensing system (102) may retrieve azimuthal scan sensing information and elevation scan sensing information from correlation between the transmitted sensing signals and the effective sensing signals, during the azimuthal scan and the elevation scan, respectively. Further, the sensing system (102) may iteratively integrate the azimuthal scan sensing information and the elevation scan sensing information obtained by performing D-number of azimuthal and elevation scans to estimate an Angle of Arrival (AoA) and a range of the one or more objects (138). The sensing system (102) may process the integrated data to identify signatures representing distinctive characteristics of the one or more objects (138) and generate a signature grid from the identified signatures. The sensing system (102) may identify patterns of the one or more objects (138) by analysing spatial and temporal relationships within the signature grid.
[0044] In an embodiment, the sensing system (102) may predict, using the AI technique, new sources in the environment based on the azimuthal scan sensing information and the elevation scan sensing information. Further, the sensing system (102) may automatically differentiate, using the AI technique, new objects and clutter sources from the new sources by analysing a phase-delay profile derived from each of the azimuthal scan sensing information and the elevation scan sensing information.
[0045] FIG. 1B illustrates an example system architecture (100B) of the sensing system (102) for detection of objects, in accordance with an embodiment of the present disclosure. Referring to FIG. 1B, in an embodiment, a waveform generator (202) may be utilized to create and evaluate waveforms used in 5G NR communication systems. The waveform generator (202) may specifically produce a 5G NRSSB signal and/or PDSCH.
[0046] In an embodiment, a Boolean logic (206) may be utilized by the sensing system (102) to ensure that the transmitted PSS, SSS, or PDSCH signals as a transmitted sensing signal beam (252) correlate with their corresponding reflected signals (desired reflected noisy signal beam (254) (from a stationary terrestrial terrain target (256)) over two alternate Orthogonal Frequency Division Multiplexing (OFDM) symbol durations for PSS and SSS, and one symbol duration for PDSCH, while filtering out other 5G NR signals. Unwanted signal from clutters (258) may be reflected back from the Rx beamforming module (228). Boolean logic (206) operates as a function of time. When the transmitter (Tx) block (214) in the sensing system (102) sends the 5G NR PSS, SSS, or PDSCH signal beam as the sensing signal beam, the Boolean logic (206) becomes true (i.e., "YES" or "logic 1"), allowing the transmitted 5G NR PSS/ SSS/ PDSCH signal to pass to a multiplexer (208). Further, when the transmitter (Tx) block (214) in the sensing system (102) sends any signal other than the 5G NR PSS, SSS, or PDSCH signal beam, the Boolean logic (206) becomes false (i.e., "NO" or "logic 0"), blocking the other signals.
[0047] In an embodiment, the multiplexer (208) may be utilized by the sensing system (102), which functions as a switch that sequentially selects one delay block at a time from a delay block-1 (210-A), a delay block-2 (210-B), and a delay block-3 (210-C). A transmitter beamforming module (212) within the sensing system (102) may be responsible for implementing beamforming during signal transmission. The primary function of the transmitter beamforming module (212) is to direct radio frequency (RF) signals toward specific target, enhancing signal strength and reducing interference. By adjusting phase and amplitude of each antenna element (250), the sensing system (102) can steer beams electronically without mechanical movement. The transmitter block (214) may transmit the sensing signal beam to a beamforming controller-1 (216) and a beamforming controller-2 (218).
[0048] In an embodiment, a binary switch (220) may select between azimuthal and elevation steering by choosing either the beamforming controller-1 (216) or the beamforming controller-2 (218). In an embodiment, the beamforming controller-1 (216) may direct the antenna array's beam horizontally across a full 360-degree azimuth, while keeping the elevation angle constant. This is achieved by modifying the phase and amplitude of signals supplied to each Tx antenna element (250), creating a beam that can be steered in the desired horizontal direction. By adjusting the phase shifts, the sensing system (102) may generate constructive and destructive interference patterns, allowing precise control over the beam's azimuthal orientation. The beamforming controller-2 (218) may adjust the beam's elevation angle, enabling vertical steering across a 180-degree range, while maintaining a constant azimuthal angle as set by the beamforming controller-1 (216). The toggling time of the binary switch (220) depends on a number of scanning iterations. In an exemplary embodiment, three iterations may be used for optimization (more iterations enhance sensing accuracy but increase the time to produce the final scanning result). Therefore, toggle times of the binary switch may be calculated as:
………………………..(1)
When the binary switch (220) is connected with the beamforming controller-1 (216),
………………….(2)
When the binary switch (220) is connected with the beamforming controller-2 (218).
[0049] The time period of the binary switch (220) is determined by the sum of its toggle times, i.e.,
…..(3)
Where;
Time taken by the sensing system (102) for one complete azimuth scanning,
= Time taken by the sensing system (102) for one complete elevation scanning,
= Processing time of delay block-1 (210-A),
= Processing time of delay block-2 (210-B),
= Processing time of delay block-3 (210-C).
[0050] Though three delay blocks are mentioned as an exemplary purpose, different number of delay blocks may be utilized in the sensing system (102) based on the requirement of vertical and horizontal scanning and use case requirements, and the toggle time may be calculated accordingly. The time period may be a critical parameter. Consequently, the time period of the binary switch (220) may also define a time granularity for the individual outputs of the sensing system (102).
[0051] In an embodiment, a splitter (222) connected to the transmitter beamforming module (212) may divide the input PSS/SSS sensing signal into multiple paths, distributing the PSS/ SSS sensing signal to various elements of the antenna array. One or more transmission magnitude and phase shifters (224-1, 224-2, ...224-N) associated with the transmitter beamforming module (212) may adjust the phase and amplitude of each signal path to control the direction and shape of the transmitted beam.
[0052] Further, a transmission antenna array (226) with transmission antenna elements (250) may radiate the processed signals into a space, forming the desired beam pattern. This may include multiple antenna elements arranged in specific geometries (e.g., linear, circular) to achieve the required coverage and beam characteristics.
[0053] In an embodiment, a receiving beamforming module (228) may receive the transmitted directed beam reflected from a terrestrial target (256). The receiving beamforming module (228) may receive radio frequency (RF) signals from specific directions. This selective reception improves sensitivity, spatial resolution, and overall system performance. Implementing receiver beamforming requires precise control over multiple RX antenna elements (230), arranged in a phased array configuration (Rx antenna array (236)). By adjusting the phase and amplitude of the received signals from each Rx antenna element (230), the sensing system (102) may focus on signals arriving from a specific direction after reflection from the targets (256). In this sensing system (102), the targets (256) may be objects that need to be detected, tracked, or identified. The targets (256) may include radar cross-sections (RCS) denoted as where L may represent the number of detected targets. The sensing system (102) may estimate the distance between the targets (256) and may further determine the target's direction in terms of azimuth and elevation angles.
[0054] The Rx antenna array (236) may capture the incoming RF signals, forming the desired beam pattern for focused reception. The Rx antenna array (236) may include multiple antenna elements (230) arranged in specific geometries (e.g., linear, planar) to achieve the required spatial resolution and angular selectivity. The incoming RF signals may include clutter with undesired echoes or reflections that hinder an identification of intended targets. The main sources of clutter may include ground clutter where reflections from terrain, buildings, and other stationary objects near the sensing system (102), which can mask low altitude targets. Clutter may include sea clutter from surface waves and maritime structures, generating reflections which cause interference in sensing. Clutter may include weather clutter with rain, fog, snow, and other atmospheric conditions that cause scattering effects, leading to interference in ISAC-based sensing. Further, clutter may also include biological clutter from birds, insects, and even human movements that create false detections in sensing applications. This is particularly important in urban ISAC deployments. Clutter may include atmospheric clutter with turbulence, temperature variations, and humidity gradients which can bend or scatter signals, affecting both sensing accuracy and communication reliability.
[0055] In an embodiment, a combiner (232) may combine the signals received from multiple Rx antenna elements (230) into a single output signal, preserving spatial information for beamforming. Further, receiving magnitude and phase shifters (234-1, 234-2…234-N) may adjust the phase and amplitude of signals received by each Rx antenna element (230) to control the direction of the receiving beam.
[0056] In an embodiment, a receiving block (240) may receive the single output signal from the combiner (232), and further perform noise filtering (242) and reception and processing (244) of the single output signal. The receiving block (240) may transmit the processed output signal to a sensing module (246). The sensing module (246) may record the processed information in a database (248).
[0057] In an embodiment, the delay blocks (delay block-1 (210-A), delay block-2 (210-B), and delay block-3 (210-C)) may be associated with the sensing module (246), and may process delays to ensure that the transmitted PSS/SSS sensing sequences align with the received PSS/SSS sequence after reflecting from the targets (256). The number of delay blocks may be determined by the required iterations of horizontal and vertical scanning cycles. These iterations may be essential for precise targets detection and effective clutter elimination. Each delay block may operate sequentially, contributing to the overall performance of the sensing system (102).
[0058] The number of delay blocks (210-A, 210-B, 210-B) may be optimized to balance accuracy and system responsiveness. The delay blocks (210-A, 210-B, 210-B) are in a sequential manner, each contributing to the timing alignment necessary for correlating transmitted PSS/SSS sensing sequences and reflected PSS/ SSS sensing sequences. The delay block-1 (210-A) may compensate for processing delays within the sensing system (102) including those from the Boolean logic (206), the multiplexer (208), the transmission (Tx) and reception (Rx) antenna arrays (226, 236), the transmission and reception blocks (214, 240), the noise filtering (242), the binary switch (220), the beamforming controller-1 (216), the beamforming controller-2 (218), excluding the round-trip delay of PSS/SSS sensing sequence. The delay block-2 (210-B) may introduce a processing delay, that is the cumulative effect of the delay introduced by the delay block-1 (210-A) and the duration of one horizontal scanning cycle, as the horizontal scanning cycle time is longer compared to the vertical scanning cycle. The delay block-3 (210-C) may introduce a processing delay that is the cumulative effect of the delays introduced by the delay block-1 (210-A), the delay block-2 (210-B), and the duration of one horizontal scanning cycle. This is because the horizontal scanning cycle time is longer compared to the vertical scanning cycle.
[0059] In an embodiment, the sensing module (246) may include a correlator, a storage and fusion block, and a decision block. A correlator (not shown here) may measure the similarity between transmitted and received signals by performing cross-correlation, estimating the time delay essential for accurate target localization by comparing reflected signals (like PSS, SSS, or PDSCH) with stored references. The storage and fusion block may collect the correlator outputs during sequential horizontal (azimuth) and vertical (elevation) scans performed by two beamforming controllers, storing the results from three cycles each and then fusing the datasets to create integrated sensing information. Further, the decision block may process the fused data using AI-driven pattern recognition techniques on signature grids to identify environmental patterns and promptly flag any anomalies, such as unexpected reflections or sudden changes, for further analysis.
[0060] In an embodiment, the database (248) may store all the RF-fingerprinting related data with the time instances. Further, the sensing module (246), while taking the decision regarding the presence of the object or while minimizing the effect of NLOS for precise calculation of object location, refers to the database (240) where channel impulse responses are also stored.
[0061] FIG. 2 illustrates an example flow diagram of a method (200) implemented by the sensing system (102) for scanning of objects, in accordance with an embodiment of the present disclosure. The method (200) may include one or more steps. The one or more steps may be performed by the sensing system (102) as illustrated in FIGs. 1A and 1B.
[0062] Referring to FIG. 2, at step 202a, the method (200) may include transmitting a 5G NR SSB beam as a sensing signal beam in an azimuthal plane, varying the azimuth angle and keeping the elevation angle constant.
[0063] At step 204a, the method (200) may include capturing noisy reflected signals corresponding to the transmitted PSS/SSS sensing signal beam, where the sensing system (102) may include a threshold value. In an embodiment, the sensing system (102) may detect and capture reflected signals originating from the transmitted PSS/SSS symbols. The transmitted beam may be directed within the azimuth (horizontal) plane, covering a range of azimuthal angles from 0° to 360° while maintaining a constant elevation angle. These reflections result from objects/environmental conditions (such as buildings, vehicles, and natural terrain), providing valuable information for sensing and analysis. The sensing system (102) has a threshold value (𝜞) to filter and analyze the reflected signals effectively, and to differentiate between significant reflections and noise. The strength and quality of these reflections may depend on the radar cross-sections (RCSs) of the reflecting surfaces and the characteristics of the transmitted signal. The receiving antenna elements (230) may be configured to process incoming signals from the azimuth plane, ensuring consistency with the transmission pattern. By applying adaptive reception beamforming techniques, the sensing system (102) may focus on specific azimuthal directions. The receiving beamforming module (228) may dynamically adjust the phase and amplitude of received signals at each antenna element (230) to form a focused reception beam within the azimuth plane. The focused beam may improve the signal-to-noise ratio (SNR) and enables precise detection of reflected signals.
[0064] At step 206a, the method (200) may include determining whether magnitude of each of the reflected sensing signals meets or exceeds the threshold value (𝜞). The threshold value corresponding to the magnitude of the reflected sensing signals may be dynamically adjusted based on one or more parameters. The one or more parameters include noise level, use case, and corresponding required accuracy level. This decision-making process may ensure that only meaningful reflections are processed further, while noise and irrelevant signals are discarded. That is, the sensing system (102) may determine that one or more first signals among the reflected sensing signals are noisy signals based on the determination that the magnitude of the reflected sensing signals is below the threshold value. Further, the sensing system (102) may discard the noisy signals from the reflected sensing signals. The sensing system (102) may operate under two possible scenarios based on the evaluation result. In a first scenario, the reflected signals with a magnitude equal to or exceeding the threshold value (𝜞) may be considered valid and significant for further processing. These signals may be typically reflections from prominent objects with sufficient radar cross-sections (RCS) to generate detectable returns. In a second scenario, reflected signals with a magnitude below the threshold value may be considered noise or irrelevant data and are rejected. These signals may originate from weak reflections caused by small objects, distant features, or environmental disturbances.
[0065] At step 208a, in response to a determination that the magnitude of each of the reflected sensing signals is less than the threshold value, the method (200) may include performing sensing range enhancement and proceeding to step 202a. In sensing range enhancement, the sensing system (102) may request a higher layer to allocate additional PREs for a newly defined enhanced sensing signal beam, which may span a single OFDM symbol or multiple OFDM symbols within the same slot. If the magnitude of the captured reflected sensing signals is below the predefined threshold value (𝜞) set by the sensing system (102), the range of objects/environmental conditions cannot be detected. This situation may arise due to poor channel conditions or a low radar cross-section of objects/environmental conditions, resulting in weak reflected sensing signal strength. In such cases, two options can be considered. In first option, the predefined threshold value (𝜞) may be decreased A second option may include enhancing the strength of the received sensing signal reflected from objects. This may be achieved by effectively increasing the power level of the PREs used in the sensing signal beam forming. The sensing system (102) may utilize either or both of the two options based on the use-case, precision requirement, and resource availability.
[0066] At step 210a, in response to the determination that the magnitude of each of the reflected sensing signals meets or exceeds the threshold value (𝜞), the method (200) may include retrieving the reflected sensing signal which may provide the azimuth plane information. In an embodiment, the sensing system (102) may determine that one or more second signals among the reflected sensing signals are the effective sensing signals based on the determination that the magnitude of the reflected sensing signals meets or exceeds the threshold value. The sensing system (102) may filter the effective sensing signals from the reflected sensing signals. That is, the sensing system (102) may retrieve and process M-numbers of reflected sensing signal (M- numbers of reflections from objects) which may provide critical azimuth plane (horizontal plane) information about the environment. After the reflected signal is captured, the receiving block (240) of the sensing system (102) may apply advanced noise filtering techniques (e.g., matched filtering) to isolate the valid reflected signal. This may significantly enhance the SNR and ensure that only relevant data is processed further. The received filtered signal may undergo multiple processing steps within the receiving block (240), including ADC conversion, cyclic prefix removal, serial-to-parallel conversion (S/P), inverse fast Fourier transform (IFFT), parallel-to-serial conversion (P/S), and demodulation.
[0067] At step 212a, the method (200) may include enabling a Boolean logic for one or more alternate 5G NR OFDM symbol durations to allow PSS and SSS signals to a correlator. In an embodiment, the sensing system (102) may use Boolean logic to allow PSS and SSS signals while blocking other 5G NR signals. The Boolean logic operates as a time-dependent function. The sensing system (102) may enable the Boolean logic to allow the sensing signals to the correlator when the sensing system (102) sends signals comprising SSB beam as the sensing signals. That is, when the transmitter (Tx) block in the sensing system (102) sends 5G NR PSS or SSS signals as the sensing signal, the Boolean logic becomes active (i.e., "YES" or logic 1) and permits the transmission of the PSS/SSS signals to the correlator. The sensing system (102) may disable the Boolean logic to block signals other than the signals comprising the SSB beam to the correlator when the sensing system sends the other signals as sensing or communication signals. That is, when the transmitter (Tx) block in the sensing system (102) sends signals other than 5G NR PSS, SSS, or PDSCH, the Boolean logic becomes inactive (i.e., "NO" or logic 0) and blocks those signals. The Boolean logic B(t) may be activated (ON) for the duration of one OFDM symbol:
B(t) = 1;
The Boolean logic B(t) may remain deactivated (OFF) for the duration of other OFDM symbols:
B(t) = 0; }
Where:
: Symbol duration
: Periodicity of the PSS/SSB signal
[0068] At step 214a, the method (200) may include performing correlation between the transmitted PSS/SSS signals and filtered reflected PSS/SSS signals with a periodicity of a binary switch for D-numbers of azimuth scanning. In an embodiment, after receiving the filtered reflected signals, which include reflections from objects with RCS , the correct reflected PSS/SSS signals may be identified by performing cross-correlation. This may include correlating the reflected PSS/SSS signals with the pre-stored PSS/SSS signal in delay blocks with periodicity of . This correlation produces a plot illustrating the varying strengths (where Sa corresponds to strength for azimuth scanning and n = total correlation samples for azimuth scanning) of correlation across different azimuthal angles. The process produces numbers of correlation peaks with corresponding strengths , representing M-numbers of reflections from objects with respective strengths. For example, if there are three PSS signals generated from m-sequences of length 127, the M-received signals are cross-correlated with these three PSS signals. To determine the correct reflected PSS sensing signals, the sensing system (102) may analyze the cross-correlation output and identify the peaks. The highest numbers of peaks indicate the correct M-PSS signals. Similarly, the SSS sequences are cross-correlated with all possible SSS sequences, which may be combinations derived from Zadoff-Chu sequences. Peaks in the cross-correlation output reveal the correct SSS sequences. Here, three delay blocks (D=3), i.e., delay block-1, delay block-2, and delay block-3 may be utilized. The binary switch may select the beamforming controller-1 for , enabling three horizontal scans. As a result, three sets of correlation data may be obtained, with each set containing numbers of peaks with respective strengths . These peaks may correspond to the azimuth (horizontal) scanning of objects and are stored in the storage and fusion block for further processing.
[0069] Further, at step 216a, the method (200) may include transmitting PSS/SSS signal beam as a sensing signal in an elevation plane with a varying elevation angle, keeping the azimuth angle constant. In an embodiment, to transmit the PSS/SSS sensing signal beam in the elevation (vertical) plane, i.e., by varying the elevation angle (ϕ) from, for example, but not limited to, 0° to 180° while keeping the azimuthal angle ( ) constant, the binary switch activates the beamforming controller-2 during . The range of the elevation angle (ϕ) may vary depending on the use-case and corresponding sensing range requirement, and may also depend on physical aspects like environment, deployment location of the sensing system (102), etc. The beamforming controller-2 may employ an adaptive beamforming algorithm to assign the appropriate magnitude and phase values to the Tx magnitude and phase shifters in the sensing system (102), ensuring the beam is steered exclusively within the azimuth plane.
[0070] At step 218a (similar to step 204a), the method (200) may include capturing the noisy reflected signal responding to the transmitted PSS/SSS signal beam. At step 220a (similar to step 206a), the method (200) may include determining if the magnitude of the reflected noisy signals is greater or equal to the threshold value. At step 222a (similar to step 208a), in response to a determination that the reflected noisy signals is less than the threshold value, the method (200) may include performing sensing range enhancement and proceeding to step 216a.
[0071] At 224a, in response to the determination that the magnitude of the reflected noisy signals is greater or equal to the threshold value, the method (200) may include retrieving the reflected sensing signal which provides the elevation plane information. In an embodiment, the sensing system (102) may retrieve and process the valid reflected sensing signal, which provides critical elevation plane (vertical plane) information about the environment. For the selected ϕ’s , elevation angle θ may be selected and the direction with maximum energy difference for the selected ϕ and θ may be determined. Thus, AoA may be converged for the received signal.
[0072] At step 226a (similar to step 212a), the method (200) may include enabling the Boolean logic for one or more alternate 5G NR OFDM symbol durations to allow the PSS and SSS signals to the correlator.
[0073] At step 228a, the method (200) may include performing correlation between the transmitted PSS/SSS signals and filtered reflected PSS/SSS signals with a periodicity of the binary switch for D-numbers of elevation scanning. In an embodiment, after receiving the filtered reflected signals, which include reflections from objects with RCS , the correct reflected PSS/SSS sensing signals may be identified by performing cross-correlation. This may include correlating the reflected PSS/SSS signals with the pre-stored PSS/SSS signal in delay blocks with periodicity of . This may include correlating the reflected PSS/SSS sensing signals with the pre-stored transmitted PSS/SSS sensing signals in delay blocks. This correlation produces a plot illustrating the varying strengths (where corresponds to strength for elevation scanning and n = total correlation samples for elevation scanning) of correlation across different elevation angles. The process produces numbers of correlation peak with corresponding strengths , representing M-numbers of reflections from objects with respective strengths.
[0074] For example, if there are three PSS signals generated from m-sequences of length 127, the M-received PSS sensing signals are cross-correlated with these three PSS signals. To determine the correct M- PSS sensing signals, analyze the cross-correlation output and identify the peaks. The highest numbers of peaks indicate the correct M-numbers of PSS signals. Similarly, the SSS sequences are cross-correlated with all possible SSS sequences, which are combinations derived from Zadoff-Chu sequences. Peaks in the cross-correlation output reveal the correct SSS sequences.
[0075] Here, three delay blocks (D=3), i.e., delay block-1, delay block-2, and delay block-3 may be utilized. The binary switch may select the beamforming controller-2 for , enabling three vertical scans. As a result, three sets of correlation data are obtained, with each set containing numbers of correlation peaks with corresponding strengths . These peaks correspond to the elevation (vertical) scanning of objects and are stored in the storage and fusion block for further processing.
[0076] At step 230a, the method (200) may include estimating AoA/DoA and a range of objects by iteratively integrating D-numbers of scanning across azimuthal and elevation angles of the received signal. At step 232a, the method (200) may include processing the identified integrated information of environmental conditions for signature generation. At step 234a, the method (200) may include preparing a signature grid from identified signatures of the integrated information. At step 236a, the method (200) may include identifying patterns in environmental conditions using signature grid by analysing spatial and temporal relationships within the signature grid.
[0077] FIG. 3 illustrates a schematic diagram (300) of transmission of a sensing signal beam by the sensing system (102) for scanning of objects, in accordance with an embodiment of the present disclosure.
[0078] In an embodiment, the PSS/SSS sensing signals may be influenced by Friis losses, absorption, and effects like reflection and scattering. To improve range and signal quality, directional transmission may be preferred over omnidirectional antennas. This can be achieved by using compact antenna arrays and beamforming, where multiple elements (e.g., ULAs or UPAs) create focused radiation patterns.
[0079] Referring to FIG. 3, in an embodiment, the PSS/SSS sensing signals (301) may be sent a DAC (302) where the PSS/SSS sensing signals (314) may undergo digital to analog conversion. Further, a RF modulator (304) may modulate the analog signals, and generate RF signals and further send the RF signals to a transmitter beamforming module (306). One or more transmission magnitude and phase shifters (308) and one or more transmission antenna elements (310) within an antenna array (312) may be included within the transmitter beamforming module (306) to process the RF signal and generate a PSS/SSS sensing signal transmitted beam (314). Beamforming of the PSS and SSS sensing signal (301) may significantly enhance the performance of the sensing system (102), its range and efficiency, by directing the signals toward specific directions. This technique involves modifying the phase and amplitude of the signals (301) transmitted from each Tx antenna element (310) in the antenna array (312) to concentrate the PSS/SSS sensing signals in a desired direction. The sensing signal beam (312) may be generated by using the transmission magnitude and phase shifters (308) to adjust the phase and amplitude of the signals transmitted from each Tx antenna element (310) in the antenna array (312), thereby focusing the sensing signals (301) precisely.
[0080] In an embodiment, to transmit the PSS/SSS sensing signal beam in the azimuth (horizontal) plane, i.e., by varying the azimuthal angle (ϕ) from 0° to 360°, while keeping the elevation angle (θ) constant, the binary switch activates the beamforming controller-1 during the . The beamforming controller-1 may employ an adaptive beamforming algorithm to assign the appropriate magnitude and phase values to the transmission magnitude and phase shifters (308) in the sensing system (102), ensuring the beam is steered exclusively within the azimuth plane. Further, the beamforming controller-1 may activate the adaptive beamforming algorithm for scanning the azimuthal plane. The beamforming controller-1 may first initialize the transmission magnitude and phase shifters (308) for the beamforming weights of each Tx antenna element (310) in the array (312), corresponding to their magnitude and phase. Initially, all weights are set to a uniform value (e.g., equal amplitude and 0° phase). For beam scanning, calculation of the phase shift required for each antenna element (310) may be based on the azimuth angle (ϕ). For an Tx antenna array of Tx antenna element, the phase shift for each element may be calculated by:

Where: d = distance between antenna elements, λ= wavelength of the signal, θ = constant elevation angle, ϕ = azimuth angle of beam steering, = position of the antenna element.
[0081] The sensing system (102) may adjust the phase shift for each antenna element (310) according to the calculated value for ϕ. For beam scanning, phase shifts may be continuously updated as the beam needs to cover a range of azimuth angles from 0° to 360°. The magnitude for each antenna element (310) may remain constant during the scanning, or adaptive algorithms like Least Mean Squares (LMS) or Conjugate Gradient (CG) may be used to adjust the magnitude to enhance beamforming performance (e.g., minimizing interference or noise). With the phase and magnitude adjustments, the beam may be steered toward the new direction, and the beam pattern may be updated for each azimuth angle. The beam may now scan in the desired direction. Further, the beam continuously scans from 0° to 360°, for each new azimuth angle, the phase and possibly magnitude may be recalculated and applied to the transmission (Tx) antenna array (312).
[0082] FIG. 4 illustrates an example flow diagram of a method (400) implemented by the sensing system (102) for calculating an AoA of the object, in accordance with an embodiment of the present disclosure.
[0083] Referring to FIG. 4, in an embodiment, the method (400) may detect and localize a new object in a known environment using the transmitter. The environment may include multiple static reflectors, for which the transmitter has a precomputed reflection map. When a new object enters the environment, the transmitter may accurately estimate the objects position, including the AoA/DoA. The presence of both Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) signal components may introduce additional complexity to the localization task.
[0084] Further, in an embodiment, the sensing system (102) may record a historical pattern of channel impulse responses in the database. Considering , as baseline channel impulse response of static environment, for various beamforming angles, azimuth angle ϕ and elevation angle θ may be recorded, where both ϕ and θ correspond to the received signal. A new impulse response after an object enters ). The difference may capture the influence of the new object.
[0085] In an embodiment, the sensing system (102) may perform a Beamformed CIR Comparison. For each beam direction ϕ, the method (400) may include identifying the difference between base channel impulse response and current channel impulse response. The method (400) may include computing a metric (e.g., energy or norm) of the difference . This reflects how much new energy has appeared in a given direction. The method (400) may include performing a coarse quadrant search by the sensing system (102). The method (400) may include beamforming and computing at four quadrants . Further, the method (400) may include selecting the direction with maximum energy difference . The method (400) may further include identifying the quadrant where the object is placed and further precising the actual object angle.
[0086] In an embodiment, the sensing system (102) may perform binary angular refinement. To perform the binary angular refinement, the method (400) may include selecting a quadrant with the maximum energy difference , and splitting further. For example, if is having maximum energy difference, the sensing system (102) may evaluate and The method (400) may include repeating this process recursively (like a binary search) to converge to the angle of arrival of the object. The method (400) may include choosing the converged azimuthal angle along with the to estimate the elevation angle. , angular error estimation. The method (400) may further include estimating the angular estimation technique, where noise is added to a known reference signal to simulate realistic channel conditions. The noisy reference may be then subtracted from the observed signal to isolate the component attributed to the target. The energy of this difference may be computed across a predefined set of angles, and the angle corresponding to the maximum energy may be selected as the estimated direction of arrival. Finally, the angular error may be determined by calculating the absolute difference between this estimated angle and the true object angle (exact angle of the one or more objects), providing a measure of estimation accuracy at that specific SNR level. The range of the objects may be estimated based on the estimated AoA and the angular error, and an accuracy of the range estimation may be identified based on the angular error.
[0087] Referring to FIG. 4, at step 402, the method (400) may include receiving the transmitted sensing signal. At step 404, the method (400) may include computing (the CIR) for various beam forming angles without the object of interest and for the fixed elevation angle. At step 406, the method (400) may include computing a new CIR after a new object enters (or new object is detected). At step 408, the method (400) may include the influence of an object for each beam direction, . At step 410, the method (400) may include computing a metric of the difference At step 412, the method (400) may include performing the coarse quadrant search by beamforming and computing at four quadrants: At step 414, the method (400) may include selecting the direction with the maximum energy difference At step 416, the method (400) may include performing binary angular refinement using a selected quadrant and splitting the beam further, If is best, evaluating and At step 418, the method (400) may include repeating the process recursively to converge the Azimuth AoA of the object. At step 420, the method (400) may include obtaining , with the estimated and with the estimated absolute error At step 422, for the selected ’s, the elevation angle θ may be selected and the steps mentioned in steps 404-418 may be repeated to find the direction with maximum energy difference for the selected ϕ and θ, thereby converging the AoA for the received signal.
[0088] In an embodiment, the method (400) may include determining pre-stored baseline channel impulse responses of the effective sensing signals for various beamforming angles, at a fixed elevation angle. The method (400) may include determining a new channel impulse response of the effective sensing signals for various beamforming angles based on a detection of an entry of the new objects into the environment. The method (400) may include identifying a difference between the pre-stored baseline channel impulse response and the new channel impulse response for each beam direction. The method (400) may include computing a metric of the difference, representing energy contributed in each beam direction. The method (400) may include performing the coarse quadrant search by computing the new channel impulse response at a set of four quadrants, and selecting at least one quadrant with maximum energy difference. The method (400) may include refining the selected quadrant by binary angular refinement to converge to the AoA of the objects.
[0089] In an embodiment, for the detection of the entry of the new objects into the environment, the method (400) may include comparing the pre-stored baseline channel impulse response and the new channel impulse response. The method (400) may determine if a difference between the pre-stored baseline channel impulse response and the new channel impulse response is greater than a predefined threshold value. Further, the method (400) may include predicting the entry of the new objects into the environment, when the difference between the pre-stored baseline channel impulse response and the new channel impulse response is greater than the predefined threshold value. The method (400) may include predicting that the difference is due to a change in environmental characteristics when the difference between the pre-stored baseline channel impulse response and the new channel impulse response is less than the predefined threshold value.
[0090] In an embodiment, to estimate the AoA and the range of the objects, the method (400) may include analysing pre-stored baseline channel impulse responses of the effective sensing signals for various beamforming angles, at a fixed azimuthal angle. The method (400) may include determining a new channel impulse response of the effective sensing signals for various beamforming angles based on a detection of an entry of the new objects into an environment. The method (400) may include identifying a difference between the pre-stored baseline channel impulse response and the new channel impulse response for each beam direction. The method (400) may include computing a metric of the difference, representing energy contributed in each beam direction. The method (400) may include performing the coarse quadrant search by computing the new channel impulse response at a set of four quadrants, and selecting at least one quadrant with maximum energy difference. The method (400) may include refining the selected quadrant by binary angular refinement to converge to the AoA of the one or more objects. The selected quadrant may be refined by splitting the beam direction of the selected quadrant into sub-directions, and recursively evaluating the new channel impulse response at each sub-direction.
[0091] FIG. 5 illustrates an example flow diagram of a method (500) implemented by the sensing system (102) for object detection, in accordance with an embodiment of the present disclosure.
[0092] Referring to FIG. 5, at step 502, the method (500) may include transmitting, by the sensing system (102), the 5G NR SSB beam as a PSS/SSS sensing signal beam in the azimuth (horizontal) plane where the azimuthal angle (ϕ) may be varied from 0° to 360°, while keeping the elevation angle (θ) constant. During this process, the binary switch activates the beamforming controller-1 during . The beamforming controller-1 utilizes the adaptive beamforming algorithm to adjust the appropriate magnitude and phase values for the Tx magnitude and phase shifters in the sensing system (102), ensuring that the beam is directed solely within the azimuth plane.
[0093] At step 504, the method (500) may include receiving 𝜷-numbers of reflection with reflected noisy signal including clutters, where the sensing system (102) has the threshold value (𝜞). The received signal includes a total of 𝜷-components, including 𝞪-components caused by reflections from the objects, which are having predefined contour like rectangular contour (like walls of a building) and (𝞫- 𝞪) components arising from clutter sources. To effectively filter and analyze the reflected signals, the sensing system (102) utilizes a predefined threshold value (𝜞).
[0094] The sensing system (102) is equipped with the Rx beamforming module, featuring -Rx antenna elements in the Rx antenna array, along with Rx magnitude and phase shifters ( ). The Rx beamforming module is designed to capture and process reflected signals, focusing on those originating from the azimuth plane to ensure alignment with the transmission pattern. Adaptive reception beamforming techniques are employed to direct the system's focus toward specific azimuthal directions. The Rx beamforming module dynamically adjusts the phase and amplitude of the signals received at each Rx antenna element, creating a focused reception beam within the azimuth plane. This focused beam significantly enhances the SNR, enabling precise detection of the reflected signals. By utilizing the threshold value (𝜞), the sensing system (102) differentiates between reflections from terrestrial targets, clutter, and noise.
[0095] At step 506, the method (500) may include determining if magnitude of the reflected noisy signal is greater than the threshold value (𝜞). At step 508, in response to the determination that magnitude of the reflected noisy signal is lesser than the threshold value (𝜞), the method (500) may include performing sensing range enhancement and proceeding to step 502.
[0096] At step 510, in response to the determination that the magnitude of the reflected noisy signal is greater than the threshold value (𝜞), the method (500) may include retrieving 𝜷- numbers of reflected noisy signal after azimuth scanning. The process of retrieving the noisy reflected signal affected by Additive White Gaussian Noise (AWGN) in the Rx block involves several key steps: signal reception, AWGN filtering, ADC conversion (ADC), cyclic prefix removal, serial-to-parallel conversion (S/P), inverse fast Fourier transform (IFFT), parallel-to-serial conversion (P/S), and demodulation. Since the reflected signal is contaminated by AWGN, effective noise filtering is essential after reception to enhance signal quality. Common techniques for filtering AWGN from noisy reflected signals include matched filtering, wavelet transform, Gaussian filtering, and exponential smoothing. These methods help improve signal quality and ensure accurate subsequent processing.
[0097] At step 512, the method (500) may include activating Boolean logic and correlating the filtered reflected sensing signal with prestored transmitted sensing signal which gives the continuous phase profile including objects and clutter sources. The Boolean logic operates as a time-dependent function. When the transmitter (Tx) block in the sensing system (102) sends 5G NR PSS or SSS signals as the sensing signal, the Boolean logic becomes active (i.e., "YES" or logic 1) and permits the transmission of the PSS/SSS signals. When the transmitter (Tx) block in the sensing system (102) sends signals other than 5G NR PSS, SSS, or PDSCH, the Boolean logic becomes inactive (i.e., "NO" or logic 0) and blocks those signals. The Boolean logic should be activated (ON) for the duration of one OFDM symbol:
B(t) = 1; …..……………………(5)
[0098] The Boolean logic BL(t) should remain deactivated (OFF) for the duration of other OFDM symbols:
B(t) = 0; }……..………(6)
Where, : Symbol duration, : Periodicity of the PSS/SSB signal.
[0099] After receiving the 𝜷-numbers of filtered reflected signals, which include 𝞪-numbers of reflections from the objects with RCS and (𝜷-𝞪) numbers of clutters. The sensing system (102) identifies the correct reflected PSS/SSS sensing signals through cross-correlation. This process involves correlating the reflected PSS/SSS signals with pre-stored PSS/SSS sensing signals in delay blocks operating with a periodicity of . The output of this correlation, when plotted, generates a graph showing the varying strengths , (where corresponds to strength for azimuth scanning and n = total correlation samples for azimuth scanning) of correlation across different azimuthal angles. This plot is known as the phase profile. Phase profile gives - numbers of correlation peaks with respective strengths , representing - numbers of reflection from the objects and numbers of clutters.
[00100] For instance, if three PSS signals are generated from m-sequences of length 127, 𝜷-numbers of received reflected sensing signals are cross-correlated with these three PSS signals. To identify the correct 𝜷-numbers of reflected signal, the system (102) analyzes the cross-correlation output to locate the peaks. The highest - numbers of peaks correspond to the correct 𝜷-numbers of reflected signal. Similarly, the SSS sequences are cross-correlated with all possible SSS sequences derived from Zadoff-Chu sequences. Peaks in the cross-correlation results indicate the correct SSS sequences.
[00101] In the present disclosure, three delay blocks (D=3), namely delay block-1, delay block-2, and delay block-3 are used. The binary switch selects the beamforming controller-1 during , enabling three horizontal scans. Consequently, three sets of correlation data for azimuth scanning are generated, with each set containing - numbers of peaks.
[00102] For example, if number of objects (𝞪) =2 and numbers of clutter sources (𝜷- 𝞪)=1, the correlation between the transmitted sensing signal and the received sensing signal results in three peaks, i.e., ( ) = 3, observed on the phase profile graph. The phase profile graph is defined as a plot of the correlation between the transmitted sensing signal and the magnitude versus the azimuthal angle. In other words, the three peaks in the graph correspond to reflections from two objects and one clutter source,
[00103] At step 514, the method (500) may include differentiating between desired targets and undesired clutters by using integrated phase delay profile of the reflected noisy sensing signal. In each azimuthal scan, the number of peaks in the output plot of correlation magnitudes or strengths versus phases, (referred to as the phase profile) represents the number of meaningful reflections, which include contributions from the objects and clutter sources. To determine the horizontal dimensions of the objects and distinguish them from clutter, the sensing system (102) introduces the concept of a phase-delay profile.
[00104] The phase-delay profile is defined as the integrated output plot of the correlation between the transmitted delayed PSS/SSS sensing signals (stored in D-number of delay blocks) and the received PSS/SSS sensing signals after reflection from terrestrial targets and clutter sources, plotted against azimuthal angles (phase profile) with different samples of times with periodicity of time.
[00105] When the binary switch selects the beamforming controller-1 during , D-number of delay blocks are sequentially activated. Each delay block delays the transmitted PSS/SSS sensing signal and stores it using Boolean logic. When the reflected PSS/SSS sensing signals are received, the delayed transmitted PSS/SSS sensing signals are sequentially correlated with the received signals for each delay block. Each delay block generates a distinct set of D-phase profiles and integrates all these phase profiles over time with periodicity to produce the phase-delay profiles, as illustrated in graphical plots (700A, 700B, 700C, 700D) of FIGs. 7A to 7D.
[00106] In the phase-delay profile, out of total - numbers of peaks, numbers of peaks, azimuth scanning process fade or vary. These fading peaks represent undesired reflections from clutter sources, while the remaining numbers of peaks remain stable, indicating desired reflections from the objects.
[00107] For example, if, =3 and =2, two peaks correspond to specific azimuth angles, e.g., and in the phase-delay profile remain constant. That represents the desired reflection from terrestrial targets. These peaks align with the same azimuth angles ( and ) in the individual phase profile, distinguishing terrestrial targets from clutter sources. Certain peaks in the individual phase profile either vary or fade away. These peaks, represented as =1, correspond to an azimuthal angle difference , as illustrated in FIGs. 6 and FIGs. 7A to 7D.
[00108] At step 516, the method (500) may include determining, from the phase delay profile of the clutter free received sensing signal reflection from 𝞪-number of targets, the azimuthal scanning information in the form of ranges and corresponding azimuthal angle differences and respective strengths , and stored in the storage and fusion block.
[00109] If the terrestrial target has a defined contour, such as a rectangular shape, the phase-delay profile of the clutter-free received 𝞪 numbers of sensing signal reflection reflected from these objects will exhibit specific patterns on the correlation plot. These patterns are characterized by meaningful strength ( ) in the neighbourhood of numbers of peaks, while the strength elsewhere remains very low. For targets with a rectangular contour, the correlation strength remains nearly constant for a specific set of azimuthal angles, referred to in this invention as the azimuthal angle difference ( ). Under the far-field approximation, the range ( ) corresponding to all azimuthal angle differences will remain approximately the same, mapping the azimuthal angle difference ( ) as: , where
……………..(7)

[00110] At step 518, the method (500) may include fixing the azimuthal angle differences as constant and transmitting the 5G NR SSB beam as the sensing signal in the vertical plane varying the elevation angle 0° to 180°.
[00111] To transmit the 5G NR SSB sensing signal beam in the vertical (elevation) plane, the elevation angle (θ) is varied continuously from 0° to 180°, while keeping the azimuthal angle differences Δ ₁, Δ ₂, …, Δ ₙ constant. During this process, the sensing system (102) activates the beamforming controller-2 during the Toggle time₂.
[00112] The beamforming controller-2 employs the adaptive beamforming algorithm, which dynamically adjusts the magnitude and phase values of the Tx magnitude and phase shifters. This ensures that the transmitted beam is directed solely within the vertical plane, and that too in specific azimuthal angle differences, allowing for precise scanning across the elevation angles. This configuration ensures uniform scanning in the elevation plane while maintaining fixed azimuthal angle differences for accurate sensing in the 3D space.
[00113] At step 520, the method (500) may include receiving, after 𝞬-numbers of reflection, the reflected noisy signal with clutters of which horizontal dimensions are known, where the sensing system (102) has the threshold value 𝜞. After 𝞬 reflections, the reflected noisy signal with clutter is received at the sensing system. The received signal consists of 𝞬-components, where 𝞪 components originate from reflections caused by terrestrial terrain targets with predefined contours, such as rectangular shapes (e.g., walls of buildings), and the remaining (𝞬- 𝞪) components arise from clutter sources. To efficiently filter and analyze these reflected signals, the sensing system applies a predefined threshold value (𝜞).
[00114] The sensing system (102) is equipped with the Rx beamforming module, comprising - Rx antenna elements in an Rx antenna array. This array features Rx magnitude and phase shifters ( , …, ), enabling precise reception and processing of the reflected signals. The Rx beamforming module is specifically designed to focus on signals originating from the elevation (vertical) plane, ensuring alignment with the transmitted elevation scanning pattern. Adaptive reception beamforming techniques are utilized, dynamically adjusting the phase and amplitude of the received signals at each Rx antenna element. This forms a focused reception beam within the vertical plane but in fixed azimuthal angle differences Δ ₁, Δ ₂, …, Δ ₙ, significantly enhancing the signal-to-noise ratio (SNR) for accurate detection of reflected signals. By employing the threshold value (𝜞), the sensing system (102) effectively distinguishes between reflections from vertical targets, clutter, and noise, allowing for accurate target detection in the elevation plane.
[00115] At step 522, the method (500) may include determining if magnitude of the reflected noisy signal is greater than or equal to 𝜞. At step 524, in response to a negative determination from step 522, the method (500) may include performing sensing range enhancement and repeating step 518. At step 526, in response to a positive determination from step 522, the method (500) may include retrieving and filtering 𝞬-numbers of reflected noisy signal after elevation scanning with fixed azimuth angle differences. At step 528, the method (500) may include activating Boolean logic and correlating the filtered reflected sensing signal with prestored transmitted sensing signal which gives the continuous phase profile including objects and clutter sources. At step 530, the method (500) may include differentiating between desired targets and undesired clutters by using integrated phase delay profile of the reflected noisy sensing signal.
[00116] At step 532, the method (500) may include determining, from the integrated phase delay profile of the clutter free reflected signal, the vertical dimensions of targets in the form of elevation angle differences for the corresponding ranges, azimuthal angle differences and respective strengths for 𝞪-number of targets.
[00117] If the terrestrial target has a defined contour, such as a rectangular shape, the integrated phase-delay profile of the clutter-free received 𝞪 sensing signal reflections from these targets will exhibit specific patterns on the correlation plot. These patterns are characterized by meaningful strength ( ) in the neighborhood of - numbers of peak, while the strength elsewhere remains very low. For targets with a rectangular contour, the correlation strength remains nearly constant for a specific set of elevation angles with corresponding azimuthal angle differences, referred to in this invention as the elevation angle difference (Δθ). Under the far-field approximation, the range ( ) corresponding to all elevation angle differences will remain approximately the same, mapping the elevation angle difference (Δθ) as: θ, where
………(8)
[00118] The time at which the maximum peaks occur is defined as the time taken for the sensing signal to travel from the transmitter beamforming module to the objects, reflect off them, and return to the receiver beamforming module. This is termed the round trip delay ( ). If - numbers peak is observed in the phase-delay profile, it corresponds to 𝞪 round trip delays. If the far-field assumption is considered, then for 𝞪- numbers of terrestrial targets, although there are 𝞪-numbers of distinct and 𝞪- numbers of distinct , they are approximately equal, i.e., . A similar analogy applies to strength as well, i.e., Therefore, the final integrated information about terrestrial features or environmental conditions can be expressed as:
……………(9)
[00119] At step 534, the method may include, detecting, by the sensing system (102), of the 𝞪-number of objects after processing the four-dimensional matrix, in the form of ranges with respective azimuthal angle differences and elevation angle differences ( , ); ( , ); ….. ( , ).
[00120] In an embodiment, the method (500) may include determining the azimuthal scanning information in a form of azimuthal angle differences and corresponding ranges and correlation strengths based on the correlation peaks associated with the objects. The method (500) may include determining vertical dimension data in a form of elevation angle differences and corresponding ranges and correlation strengths based on the correlation peaks associated with the one or more objects. The method (500) may include generating a multi-dimensional matrix to represent the range, the azimuthal and elevation angle differences, and associated correlation strength of the one or more objects. Each cell of the multi-dimensional matrix represents a spatial location of the objects and includes corresponding signature details of the objects.
[00121] That is, after processing the received data, a four-dimensional matrix , of order is generated, where represents the number of detected peaks which represent -numbers of terrestrial terrain targets and D accounts for additional processing dimensions or detection parameters that is numbers of delay blocks. The four-dimensional matrix encapsulates the following information:
R (Range): The distance to each object, calculated based on the round trip delay ( ) and the speed of light,
∠Δϕ (Azimuthal Angle Difference): The angular offset in the azimuth plane, which helps localize the target's horizontal position with horizontal dimension,
∠Δθ (Elevation Angle Difference): The angular offset in the elevation plane, which helps localize the target's vertical position with vertical dimension, and
S (Strength): The maximum correlation strength, representing the signal's reliability and strength for object detection.
[00122] Using the four-dimensional matrix, the sensing system (102) performs exact detection of 𝞪- numbers of the objects. Each detected object is characterized by a unique combination of range, azimuthal angle difference, and elevation angle difference: ( , ); ( , ); …...; ( , ).
[00123] In an embodiment, after detecting the objects, the method (500) may include comparing the pre-stored baseline channel impulse response and the new channel impulse response. The method (500) may include determining if a difference between the pre-stored baseline channel impulse response and the new channel impulse response is greater than a predefined threshold value. The method (500) may include predicting the entry of the one or more new objects into the environment, when the difference between the pre-stored baseline channel impulse response and the new channel impulse response is greater than the predefined threshold value. The method (500) may include predicting that the difference is due to a change in environmental characteristics when the difference between the pre-stored baseline channel impulse response and the new channel impulse response is less than the predefined threshold value.
[00124] This process allows the sensing system (102) to precisely determine the spatial location of each object within the sensing environment, enabling accurate mapping of their positions in terms of range, horizontal direction, and vertical direction. The structured organization of the data ensures efficient filtering and analysis, supporting clutter-free and precise detection of targets.
[00125] FIG. 6 illustrates a schematic diagram (600) of horizontal plane scanning by the sensing system (102), in accordance with an embodiment of the present disclosure.
[00126] Referring to FIG. 6, in each azimuthal scan, the number of peaks in the output plot of correlation magnitudes or strengths versus phases, (referred to as the phase profile) represents the number of meaningful reflections, which may include contributions from objects (606-1, 606-2) and clutter sources (608). The azimuthal scan may be facilitated by transmitting the sensing signal from the Tx beamforming module (610) and receiving the reflected signal by the Rx beamforming module (612).
[00127] Similarly, in each elevation scan, the number of peaks in the output plot of correlation magnitudes or strengths versus phases, (referred to as the phase profile) represents the number of meaningful reflections, which may include contributions from objects and clutter sources. The elevation scan may be facilitated by transmitting the sensing signal from the Tx beamforming module and receiving the reflected signals by the Rx beamforming module.
[00128] Therefore, the sensing system (102) provides an integrated approach to improve sensing range and accuracy by utilizing both azimuth and elevation planes for characterization of the target.
[00129] While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the invention and not as limitation.

ADVANTAGES OF THE PRESENT DISCLOSURE
[00130] The present disclosure provides an integrated approach to improve sensing range and accuracy by utilizing both azimuth and elevation planes for target localization.
[00131] The present disclosure improves clutter suppression in complex environments.
[00132] The present disclosure dynamically adjusts sensing beams to optimize performance across different environmental conditions.
[00133] The present disclosure seamlessly combines communication and sensing functionalities, leveraging Fifth Generation (5G) New Radio (NR) signals to perform both tasks efficiently without compromising system performance.
, Claims:1. A method for characterization of one or more objects using a beamforming-based Integrated Sensing and Communication (ISAC) system, comprising:
transmitting, by a sensing system (102), sensing signals comprising a synchronization signal block (SSB) beam in an azimuthal plane and an elevation plane;
capturing, by the sensing system (102), reflected sensing signals corresponding to the transmitted sensing signals;
filtering, by the sensing system (102), effective sensing signals from the reflected sensing signals based on a threshold value;
upon retrieving the effective sensing signals corresponding to the azimuthal plane and the elevation plane, correlating, by the sensing system (102), the effective sensing signals with the transmitted sensing signals for each delay block for a D-number of azimuthal scan and a D-number of elevation scan, wherein D pertains to a number of delay blocks in the sensing system (102);
retrieving, by the sensing system (102), azimuthal scan sensing information and elevation scan sensing information from correlation between the transmitted sensing signals and the effective sensing signals, during the azimuthal scan and the elevation scan, respectively;
iteratively integrating, by the sensing system (102), the azimuthal scan sensing information and the elevation scan sensing information obtained by performing D-number of azimuthal and elevation scans to estimate an Angle of Arrival (AoA) and a range of the one or more objects;
processing, by the sensing system (102), the integrated data to identify signatures representing distinctive characteristics of the one or more objects;
generating, by the sensing system (102), a signature grid from the identified signatures; and
identifying, by the sensing system (102), patterns of the one or more objects by analysing spatial and temporal relationships within the signature grid.
2. The method as claimed in claim 1, wherein the reflected sensing signals are caused by reflections from the one or more objects or environmental conditions characterized by radar cross-sections (RCS).
3. The method as claimed in claim 1, wherein filtering, by the sensing system (102), the effective sensing signals from the reflected sensing signals comprises:
determining, by the sensing system (102), whether a magnitude of each of the reflected sensing signals meets or exceeds the threshold value;
determining, by the sensing system (102), that one or more first signals among the reflected sensing signals are noisy signals based on the determination that the magnitude of the reflected sensing signals is below the threshold value;
discarding, by the sensing system (102), the noisy signals from the reflected sensing signals;
determining, by the sensing system (102), that one or more second signals among the reflected sensing signals are the effective sensing signals based on the determination that the magnitude of the reflected sensing signals meets or exceeds the threshold value; and
filtering, by the sensing system (102), the effective sensing signals from the reflected sensing signals.
4. The method as claimed in claim 3, wherein the method comprises dynamically adjusting, by the sensing system (102), the threshold value corresponding to the magnitude of the reflected sensing signals based on one or more parameters.
5. The method as claimed in claim 1, wherein prior to correlating, by the sensing system (102), the effective sensing signals with the transmitted sensing signals, the method comprises:
enabling, by the sensing system (102), a Boolean logic to allow the sensing signals to a correlator when the sensing system sends signals comprising SSB beam as the sensing signals; and
disabling, by the sensing system (102), the Boolean logic to block signals other than the signals comprising the SSB beam to the correlator when the sensing system sends the other signals as sensing or communication signals.
6. The method as claimed in claim 1, wherein to estimate the AoA and the range of the one or more objects, the method comprises:
determining, by the sensing system (102), pre-stored baseline channel impulse responses of the effective sensing signals for various beamforming angles, at a fixed elevation angle;
determining, by the sensing system (102), a new channel impulse response of the effective sensing signals for various beamforming angles based on a detection of an entry of the one or more new objects into an environment;
identifying, by the sensing system (102), a difference between the pre-stored baseline channel impulse response and the new channel impulse response for each beam direction;
computing, by the sensing system (102), a metric of the difference, representing energy contributed in each beam direction;
performing, by the sensing system (102), a coarse quadrant search by computing the new channel impulse response at a set of four quadrants, and selecting at least one quadrant with maximum energy difference; and
refining, by the sensing system (102), the selected quadrant by binary angular refinement to converge to the AoA of the one or more objects.
7. The method as claimed in claim 6, wherein refining, by the sensing system (102), the selected quadrant comprises:
splitting, by the sensing system (102), the beam direction of the selected quadrant into sub-directions; and
recursively evaluating, by the sensing system (102), the new channel impulse response at each sub-direction.
8. The method as claimed in claim 6, wherein for the detection of the entry of the one or more new objects into the environment, the method comprises:
comparing, by the sensing system (102), the pre-stored baseline channel impulse response and the new channel impulse response;
determining, by the sensing system (102), if a difference between the pre-stored baseline channel impulse response and the new channel impulse response is greater than a predefined threshold value;
predicting, by the sensing system (102), the entry of the one or more new objects into the environment, when the difference between the pre-stored baseline channel impulse response and the new channel impulse response is greater than the predefined threshold value; and
predicting, by the sensing system (102), that the difference is due to a change in environmental characteristics when the difference between the pre-stored baseline channel impulse response and the new channel impulse response is less than the predefined threshold value.
9. The method as claimed in claim 1, wherein to estimate the AoA and the range of the one or more objects, the method comprises:
analysing, by the sensing system (102), pre-stored baseline channel impulse responses of the effective sensing signals for various beamforming angles, at a fixed azimuthal angle;
determining, by the sensing system (102), a new channel impulse response of the effective sensing signals for various beamforming angles based on a detection of an entry of the one or more new objects into an environment;
identifying, by the sensing system (102), a difference between the pre-stored baseline channel impulse response and the new channel impulse response for each beam direction;
computing, by the sensing system (102), a metric of the difference, representing energy contributed in each beam direction;
performing, by the sensing system (102), a coarse quadrant search by computing the new channel impulse response at a set of four quadrants, and selecting at least one quadrant with maximum energy difference; and
refining, by the sensing system (102), the selected quadrant by binary angular refinement to converge to the AoA of the one or more objects.
10. The method as claimed in claim 6, further comprising:
estimating, by the sensing system (102), an angular error by calculating a difference between the estimated AoA and an exact angle of the one or more objects;
estimating, by the sensing system (102), the range of the one or more objects based on the estimated AoA and the angular error; and
identifying, by the sensing system (102), an accuracy of the estimation based on the angular error.
11. The method as claimed in claim 1, wherein the signature grid is generated as a multi-dimensional matrix, with each cell representing a spatial location of the one or more objects and comprising corresponding signature details of the one or more objects.
12. The method as claimed in claim 1, wherein each of the azimuthal scan sensing information and the elevation scan sensing information comprise continuous phase-delay profiles corresponding to correlation peaks associated with both the one or more objects and clutter sources, and wherein the correlation peaks that remain constant indicate the effective sensing signals from the one or more objects, and the correlation peaks that vary over time indicate signals reflecting from the clutter sources.
13. The method as claimed in claim 12, further comprising:
determining, by the sensing system (102), azimuthal scanning information in a form of azimuthal angle differences and corresponding ranges and correlation strengths based on the correlation peaks associated with the one or more objects;
determining, by the sensing system (102), vertical dimension data in a form of elevation angle differences and corresponding ranges and correlation strengths based on the correlation peaks associated with the one or more objects; and
generating, by the sensing system (102), a multi-dimensional matrix to represent the range, the azimuthal and elevation angle differences, and associated correlation strength of the one or more objects.
14. A method for scanning an environment using a beamforming-based Integrated Sensing and Communication (ISAC) system (102), comprising:
transmitting, by a sensing system (102), sensing signals comprising a synchronization signal block (SSB) beam in an azimuthal plane and an elevation plane;
capturing, by the sensing system (102), reflected sensing signals corresponding to the transmitted sensing signals;
filtering, by the sensing system (102), effective sensing signals from the reflected sensing signals based on a threshold value;
correlating, by the sensing system (102), the effective sensing signals with the transmitted sensing signals for each delay block for a D-number of azimuthal scan and a D-number of elevation scan, wherein D pertains to a number of delay blocks in the sensing system;
retrieving, by the sensing system (102), azimuthal scan sensing information and elevation scan sensing information from correlation between the transmitted sensing signals and the effective sensing signals, during the azimuthal scan and the elevation scan, respectively;
iteratively integrating, by the sensing system (102), the azimuthal scan sensing information and elevation scan sensing information obtained by performing D-number of azimuthal and elevation scans;
processing, by the sensing system (102), using an Artificial Intelligence (AI) technique, the integrated data to identify signatures representing distinctive characteristics of the environment corresponding to the azimuthal plane and the elevation plane;
generating, by the sensing system (102), a signature grid from the identified signatures; and
identifying, by the sensing system (102), patterns of one or more objects in the environment by analysing spatial and temporal relationships within the signature grid.
15. The method as claimed in claim 14, wherein in response to processing, by the sensing system (102), the integrated data, the method comprises:
continuously analysing, by the sensing system (102), using the AI technique, the identified signatures;
detecting, by the sensing system (102), a deviation in at least one signature among the identified signatures; and
promptly transmitting, by the sensing system (102), an anomaly alert to an operator of the sensing system based on the detection.
16. The method as claimed in claim 14, wherein upon generating, by the sensing system (102), the signature grid, the method comprises:
comparing, by the sensing system (102), the generated signature grid with a pre-existing signature grid;
predicting, by the sensing system (102), using the AI technique, an entry of one or more new objects into the environment based on a difference identified during the comparison; and
updating, by the sensing system (102), the generated signature grid based on the prediction.
17. The method as claimed in claim 14, further comprising:
predicting, by the sensing system (102), using the AI technique, one or more new sources in the environment based on the azimuthal scan sensing information and the elevation scan sensing information; and
automatically differentiating, by the sensing system (102), using the AI technique, one or more new objects and clutter sources from the one or more new sources by analysing a phase-delay profile derived from each of the azimuthal scan sensing information and the elevation scan sensing information.
18. The method as claimed in claim 14, further comprising:
continuously detecting, by the sensing system (102), using the AI technique, a strength or a sensing range of the effective sensing signals; and
transmitting, by the sensing system (102), using the AI technique, a control signal to a higher communication layer, requesting allocation of additional Resource Elements (REs) for generating enhanced sensing beams when the strength or the sensing range of the effective sensing signals is less than a predetermined threshold value.
19. A sensing system for characterization of one or more objects using a beamforming-based Integrated Sensing and Communication (ISAC) system (102), comprising:
a processor; and
a memory operatively coupled with the processor, wherein the memory stores instructions which, when executed by the processor, cause the system to:
transmit sensing signals comprising a synchronization signal block (SSB) beam in an azimuthal plane and an elevation plane;
capture reflected sensing signals corresponding to the transmitted sensing signals;
filter effective sensing signals from the reflected sensing signals based on a threshold value;
upon retrieving the effective sensing signals corresponding to the azimuthal plane and the elevation plane, correlate the effective sensing signals with the transmitted sensing signals for each delay block for a D-number of azimuthal scan and a D-number of elevation scan, wherein D pertains to a number of delay blocks in the sensing system;
retrieve azimuthal scan sensing information and elevation scan sensing information from correlation between the transmitted sensing signals and the effective sensing signals, during the azimuthal scan and the elevation scan, respectively;
iteratively integrate the azimuthal scan sensing information and the elevation scan sensing information obtained by performing D-number of azimuthal and elevation scans to estimate an Angle of Arrival (AoA) and a range of the one or more objects;
process the integrated data to identify signatures representing distinctive characteristics of the one or more objects;
generate a signature grid from the identified signatures; and
identify patterns of the one or more objects by analysing spatial and temporal relationships within the signature grid.