DESC:4. DESCRIPTION
Technical Field of the Invention

The present invention relates to the field of air defence systems, specifically to an advanced system capable of detecting, identifying, tracking, and neutralizing small-sized drones.

Background of the Invention

The present invention relates to the field of air defence systems and, more particularly, to an automatic drone detection and defence system (ADADS) configured to detect, identify, track, and neutralise unmanned aerial vehicles (UAVs) using integrated multi-layered countermeasures.

In recent years, the proliferation of small-sized drones has posed significant challenges to both civilian and military infrastructure security worldwide. These drones, commonly referred to as UAVs, are becoming increasingly accessible due to their affordability, ease of operation, and advanced functionalities. In conflict zones, adversaries have begun to deploy drones for reconnaissance, surveillance, and offensive operations, such as delivering explosive payloads or conducting swarm attacks on high-value targets. Their low radar cross-section, small size, low-altitude flight capability, and high manoeuvrability make them difficult to detect and intercept using conventional air defence systems designed primarily for manned aircraft or larger aerial threats.

Several prior art solutions have attempted to address this emerging threat. For example, EP3447436A1 discloses an aerial defence system comprising multiple defensive aircraft and a central control mechanism, where defensive UAVs are launched to neutralise detected threats. However, this system heavily relies on traditional manned or semi-autonomous aircraft, increasing operational complexity, cost, and deployment time. It is not optimised for detecting or neutralising small, fast-moving drones, especially in swarm scenarios.

Another prior art, RU2700107C1, describes an anti-drone combat system comprising missile launchers, artillery systems, grenade launchers, UAV suppression stations, and robotic multifunctional ground platforms. While this system adopts a multi-layered defence approach, its reliance on large mobile or stationary platforms limits rapid deployment, agility, and cost-effectiveness, especially in urban environments or tactical mobile operations. Further, its modularity and adaptability for different deployment platforms are restricted.

US10593224 provides an aerial vehicle functioning as a Ground-Based Air Defence (GBAD) platform within a Tactical Engagement Simulation (TES) environment. Although this system supports training and simulation by replicating various aircraft types, it is not designed for real-time combat scenarios requiring immediate detection and neutralisation of live drone threats.

US11697497 discloses a system involving multiple counter-attack UAVs for interception and net-based capture of drones. This approach depends heavily on drone-to-drone engagement, introducing operational delays and complexity in high-density threat environments, as the number of defensive UAVs needed scales significantly with the number of hostile drones.

Another prior art, US20180164080A1, outlines a land and air defence system comprising radar networks, central computers, and drone bases equipped with net ejector drones. While this system provides an innovative net-based interception approach, it is vulnerable to electronic warfare, signal jamming, and environmental limitations. Additionally, its dependence on net-capture may be ineffective against heavily armed or structurally resilient UAVs.

US10866597 discloses a drone detection and interception system wherein defensive drones are deployed to intercept hostile drones, and a secondary drone is deployed to locate the hostile drone controller. However, this method suffers from delays in deploying defensive drones, risking mission failure if hostile UAVs achieve their objectives before interception. Further, the system lacks integrated hard kill kinetic countermeasures and multi-layered engagement strategies.

These prior arts suffer from several disadvantages, including dependence on large or costly platforms, lack of rapid deployment adaptability across fixed, mobile, or naval installations, absence of integrated multi-layered defence mechanisms combining soft kill and hard kill options within a single system, and limited autonomous operation requiring significant human intervention. Moreover, existing systems are not designed to handle drone swarms effectively, and their response time and coverage may be inadequate in scenarios involving simultaneous multiple UAV incursions.

Therefore, there is a dire need for an improved drone detection and defence system that integrates advanced radar and sensor technologies for comprehensive detection, artificial intelligence algorithms for precise UAV classification, predictive tracking algorithms for trajectory estimation, configurable electronic warfare tools for soft kill operations, and programmable kinetic weapon systems for hard kill engagements. Such a system must provide multi-layered defence mechanisms, including blackout stages to disable communication, striking stages for physical destruction, and blazing stages deploying kamikaze UAVs to neutralise swarm attacks, all within a modular and scalable architecture adaptable to diverse operational platforms. The invention herein addresses these long-standing gaps and limitations in the prior art by providing a robust, autonomous, and technically advanced solution for countering modern drone threats.

Objects of the Invention

An object of the present invention is to provide an automatic drone detection and defence system (ADADS) that enables comprehensive detection, classification, tracking, and neutralisation of unmanned aerial vehicles (UAVs) using an integrated, modular architecture. The invention aims to address the growing threat posed by small, agile, and low radar cross-section drones that are increasingly deployed for surveillance, reconnaissance, and offensive purposes in both civilian and military domains.

Another object of the invention is to incorporate a multi-layered defence mechanism within the system, comprising a blackout stage for electronic disruption, a striking stage for kinetic neutralisation, and a blazing stage involving deployment of kamikaze UAVs for intercepting and destroying hostile drones, particularly effective against drone swarms. This layered approach ensures that threats are neutralised progressively using the most appropriate countermeasure based on the severity and nature of the threat.

A further object of the invention is to integrate advanced detection technologies, including radar, radio frequency sensors, optical sensors, infrared sensors, and acoustic sensors, enabling the system to detect UAVs under diverse environmental conditions such as low visibility, fog, or night-time operations. The invention seeks to provide reliable detection even against drones with minimal radar signatures and stealth features.

Yet another object of the invention is to employ artificial intelligence algorithms within the identification module to classify detected UAVs accurately by analysing size, shape, flight pattern, and electromagnetic signatures. This enables the system to prioritise threats based on type, capability, and operational intent, thereby enhancing overall situational awareness and response effectiveness.

An additional object of the invention is to integrate a tracking module employing predictive algorithms such as Kalman filtering or particle filtering for continuous trajectory estimation, thereby ensuring precise targeting and engagement of fast-moving UAVs. This predictive capability supports effective hard kill engagements and proactive threat neutralisation.

A still further object of the invention is to incorporate configurable electronic warfare tools within the soft kill module, such as RF jammers and GNSS spoofers with adjustable frequency bands and power levels, allowing targeted disruption of UAV communication and navigation systems with minimal collateral impact.

Another key object of the invention is to provide a hard kill module with programmable ammunition optimised for engaging small, agile UAVs, thereby enhancing engagement precision and reducing the risk of collateral damage in sensitive operational environments.

An additional object is to integrate a command and control module with data fusion and an intuitive graphical user interface, enabling centralised monitoring, threat evaluation, and real-time decision-making, while allowing manual override when required. This ensures seamless coordination between all modules and effective operator situational control.

Finally, an object of the invention is to ensure that the system is modular, scalable, and deployable across various platforms, including fixed installations, vehicle-mounted units, naval vessels, and airborne platforms, thereby offering flexible and rapid deployment capabilities suited to diverse operational requirements in modern defence and security environments.

Brief Summary of the Invention

The present invention, in one aspect, relates to an automatic drone detection and defence system (ADADS) configured to detect, identify, track, and neutralise unmanned aerial vehicles (UAVs) using an integrated and multi-layered countermeasure approach. The system combines advanced sensor technologies, artificial intelligence algorithms, electronic warfare tools, and kinetic weapon systems within a unified platform to provide comprehensive protection against UAV threats in real time.

In another aspect, the invention provides a detection module incorporating one or more of radar sensors, radio frequency sensors, acoustic sensors, optical sensors, or infrared sensors. This module enables the system to detect UAVs with low radar cross-section, operating at low altitudes or using stealth features, under diverse environmental conditions including fog, rain, or night-time.

According to a further aspect of the invention, the system comprises an identification module operatively coupled to the detection module, which classifies detected UAVs based on parameters such as size, shape, flight pattern, or electromagnetic signature. The identification module utilises artificial intelligence algorithms trained on UAV signature databases to accurately classify and assess the threat level of detected drones.

In another aspect, the invention includes a tracking module configured to track the position and trajectory of identified UAVs in real time. The tracking module employs predictive algorithms, such as Kalman filtering or particle filtering, to estimate UAV trajectories, thereby supporting proactive engagement decisions and precise targeting by kinetic countermeasure systems.

A further aspect of the invention is the provision of a soft kill module comprising electronic warfare tools including radio frequency jammers and GNSS spoofers. This module is configured to disrupt the communication and navigation systems of hostile UAVs, rendering them inoperable or forcing them to return to their launch points without causing physical destruction, thereby minimising collateral damage.

In yet another aspect, the invention comprises a hard kill module including at least one of a remote-controlled weapon station or missile launcher. This module is configured to physically neutralise UAV threats using programmable ammunition optimised for engaging small, agile, and fast-moving targets, ensuring high precision and operational effectiveness.

According to another aspect of the invention, a multi-layered defence mechanism is integrated within the system, comprising sequential engagement stages. The blackout stage engages the soft kill module to disable UAV functions, the striking stage activates the hard kill module for physical destruction, and the blazing stage deploys kamikaze UAVs configured to autonomously intercept and neutralise multiple UAV threats, particularly effective against drone swarms.

A further aspect of the invention is the inclusion of a command and control module operatively coupled to all system modules, providing centralised monitoring, data fusion, threat evaluation, and engagement decision-making. The module features an intuitive graphical user interface for real-time situational awareness and allows manual override where necessary.

In yet another aspect, the system includes a data logging module configured to record operational data related to detection, classification, tracking, and engagement, thereby facilitating post-event analysis, performance evaluation, and continuous system optimisation.

Overall, the present invention provides an autonomous, scalable, and platform-adaptable drone detection and defence system that integrates advanced technologies to deliver rapid, precise, and layered protection against evolving UAV threats in modern security and defence environments.

Brief Description of the Drawings

The above and other objects, features and advantages of the invention will become apparent from a consideration of the following detailed description presented in connection with the accompanying drawings in which:

Fig. 1 illustrates a block diagram disclosing working modules of the said anti-drone system in accordance with an exemplary embodiment of the present invention.

Fig. 2 illustrates a block disclosing operational flow of the said system in accordance with an exemplary embodiment of the present invention.

Fig. 3 illustrates a schematic view of the said anti-drone system in accordance with an exemplary embodiment of the present invention.

Detailed Description of the Invention

It is to be understood that the present invention is not limited in its application to the details of construction, arrangement of components, or methods set forth in the foregoing description or illustrated in the drawings. The invention is capable of other embodiments and of being practiced or carried out in various ways. The terminology used herein is for the purpose of description and should not be regarded as limiting.

The use of terms such as “including,” “comprising,” or “having” and variations thereof herein is meant to encompass the items listed thereafter, their equivalents, and additional items. The singular forms “a,” “an,” and “the” are intended to include plural forms as well unless the context clearly indicates otherwise. The terms “first,” “second,” and similar indicators do not denote any order or importance but are used to distinguish elements.

In accordance with exemplary embodiments of the present invention, an automatic drone detection and defence system is provided, which integrates multiple functional modules to detect, classify, track, and neutralise unmanned aerial vehicles efficiently and autonomously. The system comprises a detection module configured with advanced sensor technologies that may include radar sensors capable of detecting low radar cross-section drones, radio frequency sensors for identifying UAV communication signals, acoustic sensors to detect drone motor signatures, and optical or infrared sensors to capture visual or thermal imagery of UAVs under varying environmental conditions.

The identification module is operatively coupled to the detection module and employs artificial intelligence algorithms trained on extensive UAV signature databases to classify detected drones accurately. This classification is based on parameters such as drone size, shape, flight pattern, and electromagnetic signature characteristics. By analysing these parameters, the identification module determines the type and potential threat level posed by each detected UAV, enabling prioritised response strategies within the system.

The tracking module is configured to maintain continuous tracking of identified drones in real time. This module employs predictive algorithms, such as Kalman filtering or particle filtering techniques, to estimate the future positions and trajectories of the UAVs based on their movement patterns and velocities. Such predictive tracking capability enhances targeting accuracy and ensures that countermeasure modules receive precise positional data to engage fast-moving or manoeuvring targets effectively.

Exemplary embodiments further include a soft kill module comprising electronic warfare tools such as radio frequency jammers and GNSS spoofers. This module is configured to disrupt the communication links and navigation systems of hostile drones by jamming their control frequencies or spoofing their positional data, thereby forcing the UAVs to lose control, return to their point of origin, or enter a fail-safe landing mode. The soft kill module includes configurable settings, allowing operators to adjust frequency bands and power levels for targeted and environment-specific electronic warfare operations.

Additionally, a hard kill module is integrated within the system, comprising at least one of a remote-controlled weapon station or a missile launcher configured to physically neutralise UAV threats. The hard kill module is equipped with programmable ammunition optimised for engaging small, agile, and fast-moving drones with high precision, minimising the risk of collateral damage to surrounding infrastructure or personnel.

The system incorporates a multi-layer defence mechanism implemented through sequential operational stages. The blackout stage is configured to engage the soft kill module as an initial response to disable UAV communication and navigation systems upon detection and classification. If the UAV persists despite electronic disruption, the striking stage activates the hard kill module to engage and physically destroy the drone. In scenarios involving multiple UAVs or drone swarm attacks, the blazing stage is initiated, deploying kamikaze drones that autonomously navigate towards and intercept hostile drones through impact detonation, thereby providing an effective final neutralisation layer against mass UAV incursions.

The command and control module of the system is operatively coupled to all other modules and serves as the central hub for integrated monitoring, data fusion, threat evaluation, and engagement decision-making. It is configured with an intuitive graphical user interface that displays real-time operational data, threat classifications, tracking trajectories, and countermeasure status, while enabling manual override controls for operator intervention when required.

Further, the system includes a data logging module configured to record all operational events, including detection signals, classification outcomes, tracking data, and countermeasure engagements. This data is utilised for post-event analysis, system performance evaluation, and continuous optimisation of detection algorithms, classification accuracy, and engagement protocols to enhance future operational effectiveness.

In these exemplary embodiments, the system is designed to operate autonomously with minimal human intervention while retaining manual override capabilities for critical decision-making. It is configured to be modular and scalable, enabling deployment across various platforms, including fixed installations for critical infrastructure protection, vehicle-mounted units for tactical mobile operations, and naval vessels for maritime UAV defence scenarios. This ensures that the system delivers a robust, adaptive, and comprehensive solution for modern aerial threat environments involving unmanned aerial vehicles.

The present invention provides an automatic drone detection and defence system (ADADS) (100) designed to detect, identify, track, and neutralise unmanned aerial vehicles (UAVs) using integrated multi-layered countermeasures. Referring to Fig. 1, the system (100) comprises multiple interconnected modules, each performing critical functions to deliver comprehensive drone defence.

The detection module (1), as shown in Fig. 1, includes advanced radar sensors configured to detect UAVs with low radar cross-section signatures. The radar uses electronically scanned or mechanically steered antenna arrays to scan the airspace over a wide angular range, providing azimuth, elevation, and range data for detected objects. The detection module (1) also integrates radio frequency sensors capable of scanning wideband frequencies to detect and analyse communication signals between UAVs and their ground control centres. Acoustic sensors within this module detect characteristic UAV motor and propeller noise signatures, while optical and infrared sensors capture visual and thermal imagery, enabling detection under low visibility, night-time, or camouflage conditions.

The identification module (2), coupled to the detection module (1) as illustrated in Fig. 1, processes sensor data to classify detected UAVs. It employs artificial intelligence algorithms, particularly convolutional neural networks (CNNs), trained on UAV signature datasets comprising radar cross-section patterns, RF frequencies, acoustic profiles, and visual silhouettes. For example, CNN models process real-time imagery frames from optical sensors to identify UAV types, while an RF signature classification algorithm compares detected frequencies to stored spectral templates, determining the make, model, and threat potential of the UAV.

The tracking module (3), also shown in Fig. 1, continuously tracks the position and movement trajectory of identified UAVs. It uses predictive tracking algorithms, such as Kalman filtering, to estimate UAV state vectors (position, velocity) and predict future positions by updating the state estimate using sensor measurements and motion models. For non-linear or evasive UAV flight behaviours, particle filtering is implemented, sampling multiple state hypotheses and adjusting their probabilities based on sensor likelihood functions. This ensures robust tracking for agile drones executing sudden manoeuvres or velocity changes.

The soft kill module (4), depicted in Fig. 1, comprises electronic warfare tools including drone RF jammer (11) and GNSS spoofers. The RF jammer is configured to transmit high-power interference signals across user-defined frequency bands, effectively disrupting UAV command and control links. The GNSS spoofer generates manipulated navigation signals, causing UAVs to drift from their intended path, enter fail-safe landing protocols, or return to their point of origin. Configurable jammer settings within the command and control module (6) allow operators to select frequencies and output power levels for targeted disruption while avoiding unintended interference with other spectrum users.

The hard kill module (5), shown in Figure 1, includes a remote-controlled weapon station (9) and a missile launcher (10). The weapon station (9) is equipped with stabilised mounts and high-precision targeting systems. Fire control algorithms compute ballistic trajectories accounting for target range, speed, wind drift, and projectile characteristics to maximise hit probability. The missile launcher (10) is configured for extended range engagements, deploying guided munitions with proximity fuses that detonate near fast-moving UAVs, ensuring physical neutralisation even when direct impact is not feasible.

The multi-layer defence mechanism (25) is shown in Fig. 2 and is structured into sequential operational stages. The blackout stage (26) represents the initial line of defence, activating the soft kill module (4) to jam communication links and spoof navigation systems upon UAV detection. If the UAV threat persists beyond electronic disruption, the striking stage (27) is initiated, activating the hard kill module (5) to engage and destroy the UAV physically. In high-density threat environments involving multiple drones or swarms, the blazing stage (28) is engaged, deploying kamikaze UAVs (12) shown in Fig. 1. These kamikaze drones autonomously navigate to intercept hostile UAVs using onboard flight control algorithms, such as Rapidly-exploring Random Trees (RRT) for obstacle avoidance path planning, and detonate upon impact using integrated warheads, neutralising UAV swarms effectively.

The command and control module (6), as depicted in Fig. 1, serves as the operational core, integrating data from all detection, identification, tracking, and countermeasure modules. It employs multi-sensor data fusion algorithms, such as Bayesian filters, to combine radar, RF, acoustic, optical, and infrared data into a unified situational awareness map. The module (6) features an intuitive graphical user interface displaying threat zones, UAV classifications from the identification module (2), real-time tracking trajectories from the tracking module (3), and status indicators for the soft kill module (4), hard kill module (5), and blazing stage (28) kamikaze drone deployment. Embedded control algorithms prioritise threats based on UAV type, flight behaviour, proximity, and potential payload assessment, enabling automated countermeasure selection while allowing operator override when necessary.

The data logging module (26), as shown in Fig. 2, records all operational data, including detection signals, classification outcomes, tracking data, countermeasure deployments, and command decisions. Data is stored with timestamped entries in structured databases and is used to retrain AI models in the identification module (2) and tracking module (3), enhancing detection accuracy and classification confidence through supervised learning updates and reinforcement learning fine-tuning for dynamic operational environments.

Referring to Fig. 3, the method of operation for the system begins with system initiation and self-calibration, where each sensor and module performs baseline functionality checks. Referring to Figs 1-3, upon drone detection by the detection module (1), the identification module (2) classifies the UAV, and the tracking module (3) predicts its trajectory. The command and control module (6) evaluates the threat and engages the blackout stage (26) by activating the soft kill module (4). If the UAV remains operational, the striking stage (27) is activated, deploying the hard kill module (5) to engage and neutralise the UAV. In scenarios involving multiple UAV threats or swarm attacks, the blazing stage (28) is initiated, deploying kamikaze UAVs (12) to autonomously intercept and destroy the threats. Throughout this operation, the command and control module (6) ensures seamless coordination of all subsystems, while the data logging module (26) continuously records operational data for post-event analysis and system optimisation.

Best Method of Operation
The best method of operating the automatic drone detection and defence system (ADADS) begins with system initiation and self-calibration processes. Upon powering on, the command and control module (6) executes an automated diagnostic routine where each subsystem, including the detection module (1), identification module (2), tracking module (3), soft kill module (4), hard kill module (5), and data logging module (26), performs baseline functionality checks to ensure readiness. The radar sensors within the detection module (1) calibrate their scanning parameters, adjusting beamforming angles or mechanical steering limits to optimise coverage, while the radio frequency sensors initialise spectral scanning ranges.

During live operation, the detection module (1) continuously scans the airspace using radar to detect UAVs based on reflections and Doppler shifts, and uses RF sensors to identify command and control signals emitted by UAVs communicating with ground control stations. Acoustic sensors simultaneously listen for characteristic drone motor and propeller noise signatures, while optical and infrared sensors capture continuous visual and thermal imagery to detect UAVs under low-light or obstructed conditions.

Upon detection of an aerial object, the identification module (2) is activated. Data from all sensors is processed using artificial intelligence algorithms. Visual data frames are analysed by convolutional neural networks trained on UAV image datasets to determine shape and type. RF signature classification algorithms match detected frequencies against the onboard database to identify the manufacturer, model, and communication protocol used by the UAV. The identification module (2) fuses these features to classify the drone, assess its threat level, and pass the data to the command and control module (6).

The tracking module (3) is engaged once a UAV is classified. It uses Kalman filtering algorithms to estimate the drone's current position and velocity vectors based on incoming sensor data. The filter updates its predictions at each time step, compensating for sensor noise and environmental disturbances, to provide real-time trajectory estimation. For drones exhibiting non-linear movement patterns or evasive manoeuvres, particle filtering algorithms are used to sample multiple hypotheses of position and velocity, updating their weights based on sensor likelihood functions, providing robust tracking even during complex aerial manoeuvres.

The command and control module (6) integrates data from the detection module (1), identification module (2), and tracking module (3) through multi-sensor data fusion algorithms. It presents this information via a graphical user interface that displays UAV positions, classifications, trajectory paths, and system status. The embedded decision-making algorithms evaluate UAV threat levels based on classification, proximity to protected zones, velocity, and potential payload type.

If a UAV is classified as a threat, the system initiates the multi-layer defence mechanism (25) starting with the blackout stage (26). The command and control module (6) activates the soft kill module (4), instructing the RF jammer to transmit interference signals over the detected UAV communication frequency bands. Simultaneously, the GNSS spoofer is engaged to broadcast manipulated navigation signals to confuse the UAV's positioning system, potentially forcing it into fail-safe landing or return-to-home protocols.

If the UAV remains operational despite electronic disruption, the striking stage (27) is initiated. The command and control module (6) activates the hard kill module (5), prioritising between the remote-controlled weapon station (9) and the missile launcher (10) based on target range and engagement feasibility. For close to mid-range UAVs, the remote-controlled weapon station (9) uses fire control algorithms to calculate projectile trajectories accounting for target speed, distance, elevation, and environmental conditions such as wind drift, executing precision kinetic engagement. For distant or high-speed UAVs, the missile launcher (10) deploys guided munitions equipped with proximity fuses, ensuring detonation near the target for effective neutralisation.

In scenarios where multiple UAVs are detected simultaneously or in swarm formations, the blazing stage (28) is activated. The command and control module (6) deploys kamikaze UAVs (12) configured with onboard flight controllers implementing Rapidly-exploring Random Trees (RRT) path planning algorithms. These kamikaze drones autonomously calculate interception paths towards assigned hostile UAV targets while avoiding obstacles or friendly aerial assets, detonating upon impact using integrated warheads to neutralise swarm threats effectively.

Throughout the entire operation, the data logging module (26) records detection data, classification results, tracking vectors, countermeasure deployments, and command decisions with timestamped entries. This operational data is stored in structured databases and is used post-operation to retrain the AI models within the identification module (2) and tracking module (3), improving classification accuracy and trajectory prediction performance for future deployments.

The system is designed to operate autonomously with minimal human intervention, while enabling manual override of countermeasure activations via the graphical user interface within the command and control module (6). This ensures operational flexibility, enabling operators to adapt defence strategies to mission requirements or tactical directives in real time, thereby constituting the best method of operation for the system (100) in diverse defence, security, and critical infrastructure protection environments.

Alternative Embodiments and Methods
In alternative embodiments of the present invention, the detection module (1) may be configured with dual-band radar sensors, such as integrating both X-band and Ku-band radars, to enhance detection fidelity against UAVs with varying radar cross-sections and flight profiles. The inclusion of Ku-band radars provides higher frequency detection with narrower beamwidths, allowing precise localisation in high-clutter environments, while X-band radars provide broader coverage and improved detection of small, low-altitude drones.

Another alternative embodiment includes configuring the radio frequency sensors within the detection module (1) with software-defined radio (SDR) architectures. This allows adaptive scanning of dynamic frequency bands, enabling the system to detect UAV communication protocols that use frequency hopping spread spectrum (FHSS) or direct-sequence spread spectrum (DSSS) technologies, thereby overcoming traditional fixed-frequency RF detection limitations.

In yet another embodiment, the identification module (2) may implement recurrent neural networks (RNNs) or transformer-based AI models to analyse temporal sequences of UAV flight behaviour data. This enhances the system’s ability to identify drones based on movement patterns over time rather than static feature analysis alone, improving classification accuracy against stealth UAVs employing deceptive or low-emission flight tactics.

The tracking module (3) may alternatively implement an Extended Kalman Filter (EKF) to handle non-linear UAV movement models with greater computational efficiency compared to standard particle filtering approaches. In embodiments where processing resources permit, Unscented Kalman Filters (UKF) or deep learning-based object tracking algorithms, such as Long Short-Term Memory (LSTM) networks for trajectory prediction, may also be utilised to handle high-speed agile manoeuvres with minimal estimation error.

In certain alternative configurations, the soft kill module (4) may integrate directed energy systems, such as microwave emitters, as a complementary electronic warfare tool alongside RF jammers and GNSS spoofers. The microwave emitter generates high-power pulses to disrupt UAV onboard electronics or sensors without requiring kinetic force, providing an additional non-destructive neutralisation layer particularly effective against commercial drones with minimal electromagnetic shielding.

The hard kill module (5) may alternatively include directed energy weapons, such as high-energy laser systems, mounted alongside the remote-controlled weapon station (9) and missile launcher (10). Laser weapons are configured to deliver focused energy beams capable of damaging UAV structural components or optical sensors, providing silent and precise neutralisation without the acoustic or kinetic signature of conventional firearms or missiles.

In another embodiment, the multi-layer defence mechanism (25) stages may be dynamically reconfigured based on operational doctrine. For example, the system may initiate the striking stage (27) as the first line of defence against identified armed drones without first engaging the blackout stage (26), particularly in battlefield or high-threat environments where immediate kinetic engagement is prioritised over electronic disruption.

The command and control module (6) may alternatively be deployed as a distributed architecture, where multiple detection, tracking, and countermeasure modules are networked across a wide geographical area. Each module operates autonomously for local threat neutralisation, while data is synchronised to a centralised command module for regional situational awareness and coordinated defence strategies.

In alternative operational methods, the system (100) may be integrated with external threat intelligence feeds, such as UAV registration databases, air traffic management systems, or military radar networks. This integration enables pre-emptive identification of authorised drones and prioritises engagement only against unregistered or hostile UAVs, optimising resource utilisation and reducing false positive engagements.

Additionally, alternative embodiments include deploying the system on mobile naval platforms, such as patrol vessels, where the detection module (1), identification module (2), tracking module (3), and countermeasure modules (4, 5) are stabilised with gimballed mounts to compensate for vessel pitch and roll. This configuration ensures accurate detection, tracking, and engagement under maritime operational conditions.

In yet another operational method, the blazing stage (28) kamikaze UAVs (12) may be programmed with cooperative swarm algorithms, enabling multiple kamikaze drones to coordinate their interception paths and targets using consensus-based distributed decision-making, thereby maximising effectiveness against large-scale drone swarm attacks.

These alternative embodiments and methods are illustrative of the flexibility, scalability, and modularity of the automatic drone detection and defence system (100), and it is understood that various modifications, substitutions, and alterations can be made to the system architecture, algorithms, and operational workflows without departing from the scope of the present invention as claimed.

Applications of the Invention
The automatic drone detection and defence system (ADADS) (100) finds application across a diverse range of operational scenarios. In military installations, the system (100) is deployed at forward operating bases and border outposts to detect and neutralise surveillance or armed UAVs used by adversaries for reconnaissance or offensive missions. The detection module (1) identifies low radar cross-section drones attempting to approach undetected, while the identification module (2) classifies them, enabling rapid activation of soft kill module (4) or hard kill module (5) based on threat severity.

In critical infrastructure protection, such as airports, oil refineries, and nuclear power plants, the system (100) ensures safety against unauthorised UAV incursions that may threaten operational security or safety standards. The command and control module (6) integrates with facility security networks to issue automated alerts and engage the blackout stage (26) for non-destructive neutralisation of hobbyist or commercial drones entering restricted airspace.

For urban security and law enforcement applications, the system (100) is mounted on vehicle-based platforms to provide mobile drone defence during high-profile events, public gatherings, or VIP convoy movements. The tracking module (3) ensures continuous tracking of drones manoeuvring between buildings or urban obstacles, while the hard kill module (5) with remote-controlled weapon station (9) engages drones if electronic disruption is ineffective.

In naval operations, the system (100) is integrated onto patrol vessels or frigates, where the detection module (1) and tracking module (3) operate on stabilised gimballed mounts to maintain target lock under vessel movement. The blazing stage (28) with kamikaze UAVs (12) is deployed for swarm defence against coordinated drone attacks targeting naval assets.

Additionally, the system (100) is suited for air defence of temporary forward command posts, where its modular architecture allows rapid setup and tear-down, ensuring force protection in dynamic battlefield environments.

Advantages of the Invention
The invention provides several technical and operational advantages. The integration of multiple detection modalities within detection module (1) ensures reliable UAV detection under diverse environmental and operational conditions. The use of artificial intelligence within identification module (2) enables high-confidence classification, supporting rapid and informed countermeasure decisions.

The tracking module (3) employing Kalman filtering or particle filtering ensures robust trajectory estimation of fast-moving and agile UAVs, enhancing hard kill engagement precision. The inclusion of configurable electronic warfare tools within soft kill module (4) provides targeted, non-destructive neutralisation options, while the programmable ammunition within hard kill module (5) minimises collateral damage.

The multi-layer defence mechanism (25), comprising blackout stage (26), striking stage (27), and blazing stage (28), offers escalating countermeasure responses, optimising resource usage while ensuring complete neutralisation of threats. The kamikaze UAVs (12) in the blazing stage (28) provide an effective solution against drone swarms, addressing an operational gap in existing air defence systems.

The command and control module (6) offers centralised monitoring, multi-sensor data fusion, and an intuitive graphical user interface, enabling autonomous operation with manual override as needed. The data logging module (26) ensures operational transparency, traceability, and supports continuous system improvement through AI model retraining.

Overall, the system (100) delivers a modular, scalable, and adaptable solution deployable across fixed, mobile, and naval platforms, enhancing operational flexibility and force protection capabilities.
Test Standards and Results
The performance evaluation of the system (100) was conducted under controlled and operationally realistic conditions adhering to applicable defence and electronic warfare test standards. The detection module (1) was tested against UAVs with radar cross-sections as low as 0.01 m² at distances up to 9 km, demonstrating reliable detection capability with minimal false positives.

The identification module (2) was evaluated using a dataset comprising diverse UAV models, achieving classification accuracy exceeding 95% under varying lighting and environmental conditions. The tracking module (3) employing Kalman filtering algorithms maintained trajectory estimation accuracy within ±1 meter deviation for UAVs moving at speeds up to 120 km/h.

The soft kill module (4) demonstrated effective jamming of UAV communication links and GNSS spoofing within operational ranges up to 2 km line-of-sight, resulting in targeted UAVs entering fail-safe or return-to-home modes in over 90% of test scenarios. The hard kill module (5), including remote-controlled weapon station (9) and missile launcher (10), achieved a target hit rate exceeding 92% against stationary and moving UAV targets within engagement ranges up to 825 meters for kinetic systems and up to 3.5 km for missile engagements.

The blazing stage (28) deploying kamikaze UAVs (12) successfully neutralised UAV swarm formations, with individual kamikaze drones achieving successful interception and impact detonation against assigned targets in controlled swarm simulations.

All test results validate the system (100)’s operational readiness, performance reliability, and suitability for deployment under defence and homeland security operational standards.

All features described herein may be replaced by alternative features serving the same, equivalent, or similar purpose, and any such substitutions are intended to be within the scope of the invention. The invention encompasses all modifications, variations, and equivalents falling within the spirit and scope of the appended claims.

Nothing in this specification should be construed as an admission of common general knowledge or as an admission that any prior art forms part of the state of the art in any jurisdiction.
,CLAIMS:5. CLAIMS
We Claim
1. An automatic drone detection and defence system (100), comprising:
a detection module (1) configured to detect unmanned aerial vehicles (UAVs) using one or more of radar sensors, radio frequency sensors, acoustic sensors, optical sensors, or infrared sensors;
an identification module (2) operatively coupled to the detection module (1) and configured to classify detected UAVs based on parameters including size, shape, flight pattern, or electromagnetic signature;
a tracking module (3) configured to track the position and trajectory of identified UAVs in real time using predictive algorithms;
a soft kill module (4) comprising electronic warfare tools including radio frequency jammers and GNSS spoofers configured to disrupt communication and navigation systems of UAVs;
a hard kill module (5) comprising at least one of a remote-controlled weapon station (9) or a missile launcher (10) configured to physically neutralise UAV threats;
a command and control module (6) operatively coupled to the detection module (1), identification module (2), tracking module (3), soft kill module (4), and hard kill module (5), and configured for centralised monitoring, data fusion, threat evaluation, and engagement decision-making, further providing a graphical user interface;
a data logging module (26) configured to record detection, classification, tracking, and engagement data for post-event analysis;
Characterized in that,
the system (100) comprises a multi-layer defence mechanism (25) including:
a blackout stage (26) engaging the soft kill module (4) to disable UAV communication and navigation systems during initial intrusion;
a striking stage (27) activating the hard kill module (5) to physically destroy UAVs that persist after the blackout stage (26);
a blazing stage (28) deploying kamikaze UAVs (12) configured to autonomously intercept and neutralise UAV threats through impact detonation, particularly effective against UAV swarms;
the detection module (1) comprises an X-band radar with mechanically steered or electronically scanned antenna arrays configured to detect low radar cross-section UAVs up to 9 km range;
the identification module (2) comprises artificial intelligence algorithms trained on a UAV signature database to classify UAV types and assess threat levels;
the tracking module (3) employs Kalman filtering or particle filtering algorithms for continuous trajectory prediction to enhance targeting accuracy;
the soft kill module (4) comprises configurable jammer settings allowing frequency band and power level adjustments for targeted electronic warfare operations;
the hard kill module (5) utilises programmable ammunition optimised for engaging small, agile, and fast-moving UAV targets;
the command and control module (6) integrates multi-sensor data fusion to display threat zones, classification results, tracking trajectories, and countermeasure status via the graphical user interface; and
the modules are configured for deployment in at least one of fixed installations, vehicle-mounted platforms, or naval platforms, wherein the system (100) is configured to operate autonomously or under manual control to provide integrated detection, classification, tracking, and multi-layered countermeasure deployment against single or multiple UAV threats.

2. The system (100) as claimed in claim 1, wherein the detection module (1) comprises an X-band radar with a mechanically steered or electronically scanned antenna array configured to detect UAVs having low radar cross-section up to a range of 9 km.

3. The system (100) as claimed in claim 1, wherein the identification module (2) comprises artificial intelligence algorithms trained on a UAV signature database to classify UAV types, assess threat levels, and differentiate drones from non-threat aerial objects.

4. The system (100) as claimed in claim 1, wherein the tracking module (3) employs Kalman filtering or particle filtering algorithms for continuous trajectory prediction, enhancing targeting accuracy of the hard kill module (5).

5. The system (100) as claimed in claim 1, wherein the soft kill module (4) comprises configurable jammer settings allowing user-defined frequency bands and power levels for targeted electronic warfare against UAV command and control links.

6. The system (100) as claimed in claim 1, wherein the hard kill module (5) utilises programmable ammunition optimised for engaging small, agile, and fast-moving UAV targets, thereby increasing precision and minimising collateral damage.

7. The system (100) as claimed in claim 1, wherein the command and control module (6) integrates multi-sensor data fusion and provides a graphical user interface displaying threat zones, classification results, tracking trajectories, and countermeasure status in real time.

8. The system (100) as claimed in claim 1, wherein the blazing stage (28) comprises deployment of kamikaze UAVs (12) equipped with impact-detonation warheads and autonomous navigation to intercept and neutralise multiple UAV threats effectively.

9. The system (100) as claimed in claim 1, wherein the modules are configured for deployment in at least one of fixed installations, vehicle-mounted platforms, naval platforms, or airborne platforms to provide flexible operational adaptability.

10. A method of operating an automatic drone detection and defence system (100) as claimed in claim 1, comprising the steps of:
detecting UAVs using the detection module (1);
classifying detected UAVs using the identification module (2);
tracking positions and predicting trajectories of classified UAVs using the tracking module (3);
evaluating threat levels using the command and control module (6);
engaging the soft kill module (4) in the blackout stage (26) to disrupt UAV communication and navigation systems;
if the UAV threat persists, engaging the hard kill module (5) in the striking stage (27) to physically neutralise UAVs;
deploying kamikaze UAVs (12) in the blazing stage (28) to intercept and destroy UAV threats; and
recording operational data using the data logging module (26) for post-event analysis and system optimisation.

6. DATE AND SIGNATURE

Dated this on 08th July 2025 Signature

Mr. Srinivas Maddipati
In house Patent Agent-IN/PA 3124,
For., Zen Technologies Limited