DESC:TECHNICAL FIELD
[0001] The present disclosure relates, in general, to signal processing, and more specifically, relates to cloud clutter mitigation in marine surface target detection.

BACKGROUND
[0002] Long-range sensing plays an important role in detecting boats and ships in marine surface vessel traffic systems. Apart from picking up boat/ship signatures, these sensors also pick up signatures of unwanted clutter from land, sea surface and precipitation/clouds. The precipitation or cloud clutter gives rise to a huge number of false detections and sometimes leads to the loss of true targets. Predominantly precipitation clutter is reduced by lowering the grazing angle of the radar or by changing polarization. However, this method reduces target detection capabilities and still lets some residue of precipitation clutter leak into the echo signal.
[0003] Therefore, it is desired to overcome the drawbacks, shortcomings, and limitations associated with existing solutions, and develop a system that reduces the impact of precipitation or cloud clutter on target detection and tracking by dynamically detecting and suppressing cloud clutter.

OBJECTS OF THE PRESENT DISCLOSURE
[0004] An object of the present disclosure relates, in general, to signal processing, and more specifically, relates to a cloud clutter mitigation in marine surface target detection.
[0005] Another object of the present disclosure is to provide a system that reduces the impact of precipitation or cloud clutter on target detection.
[0006] Another object of the present disclosure is to provide a system that dynamically detects, tags clutter-affected zones and suppress cloud/precipitation clutter digitally without impacting the detection capability of the surveillance system.
[0007] Yet another object of the present disclosure is to provide a system that detects clouds dynamically and gives out precise cloud contour with respect to the radar returns to assist the tracker in avoiding the formation of false tracks by creating track inhibit zones without masking the true targets.

SUMMARY
[0008] The present disclosure relates in general, to signal processing, and more specifically, relates to cloud clutter mitigation in marine surface target detection. The main objective of the present disclosure is to overcome the drawbacks, limitations, and shortcomings of the existing system and solution, by providing a system that dynamically detects, tags clutter-affected zones and suppresses cloud/precipitation clutter digitally without impacting the detection capability of the surveillance system.
[0009] The module detects clouds dynamically and gives out precise cloud contour with respect to the radar returns so as to assist the tracker to avoid the formation of false tracks by creating track-inhibit zones without masking the true targets. The detected cloud contours may follow the trajectory of the cloud movement so that the inhibit zones get cleared as soon as the cloud has dispersed. The detected cloud contour is processed to estimate the appropriate threshold to suppress the cloud echoes and enhance target detection capabilities inside cloud clutter-affected areas. The module takes a coastline map as input for aiding in detecting cloud contours using the density of raw video, spread and symmetry of shape.
[0010] The system for mitigating precipitation clutter in an area of interest, includes a radar configured to receive a set of data pertaining to radar reflections in range, azimuth, resolution and pulse width of the area of interest. A processor operatively coupled with the radar, the processor is configured to receive, from the radar, the set of data of the area of interest to quantize the received set of data. The processor computes space-time clutter (STC) based on the quantized data by masking land clutter data, wherein the land clutter data pertains to reflections from buildings, mountains and any combination thereof.
[0011] The processor computes threshold values based on the range and azimuth of the received set of data. Interpolate the computed STC and the threshold values of the range and azimuth to suppress sea clutter data. Convert the sea clutter data and land cutter data to range-azimuth bin form. Compress, by a detection logic, the range-azimuth bin form in azimuth domain. Apply data clustering mechanism to the compressed data to detect zones of echo data to filter the zones aiding in distinguishing between targets and undesired clutter.
[0012] Various objects, features, aspects, and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWINGS
[0013] The following drawings form part of the present specification and are included to further illustrate aspects of the present disclosure. The disclosure may be better understood by reference to the drawings in combination with the detailed description of the specific embodiments presented herein.
[0014] FIG. 1 illustrates an exemplary block diagram of precipitation clutter detection and suppression system, in accordance with an embodiment of the present disclosure.
[0015] FIG. 2 illustrates an exemplary flow chart of a method for mitigating precipitation clutter in an area of interest, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION
[0016] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. If the specification states a component or feature “may”, “can”, “could”, or “might” be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic.
[0017] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[0018] The present disclosure relates, in general, to signal processing, and more specifically, relates to cloud clutter mitigation in marine surface target detection. The proposed system disclosed in the present disclosure overcomes the drawbacks, shortcomings, and limitations associated with the conventional system by providing a system that dynamically detects, tags clutter-affected zones and suppress cloud/precipitation clutter digitally without impacting the detection capability of the surveillance system. The module detects clouds dynamically and gives out precise cloud contour with respect to the radar returns so as to assist the tracker to avoid the formation of false tracks by creating a track-inhibit zones without masking the true targets. The detected cloud contours may follow the trajectory of the cloud movement so that the inhibit zones get cleared as soon as the cloud has dispersed. The detected cloud contour is processed to estimate the appropriate threshold to suppress the cloud echoes and enhance target detection capabilities inside cloud clutter-affected areas. The module takes a coastline map as input for aiding in detecting cloud contours using the density of raw video, spread and symmetry of shape. The present disclosure can be described in enabling detail in the following examples, which may represent more than one embodiment of the present disclosure.
[0019] The advantages achieved by the system of the present disclosure can be clear from the embodiments provided herein. The system reduces the impact of precipitation or cloud clutter on target detection that dynamically detects, tags clutter-affected zones and suppress cloud/precipitation clutter digitally without impacting the detection capability of the surveillance system. The system detects clouds dynamically and gives out precise cloud contour with respect to the radar returns so as to assist the tracker to avoid the formation of false tracks by creating track-inhibit zones without masking the true targets. The description of terms and features related to the present disclosure shall be clear from the embodiments that are illustrated and described; however, the invention is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions, and equivalents of the embodiments are possible within the scope of the present disclosure. Additionally, the invention can include other embodiments that are within the scope of the claims but are not described in detail with respect to the following description.
[0020] FIG. 1 illustrates an exemplary block diagram of precipitation clutter detection and suppression system, in accordance with an embodiment of the present disclosure.
[0021] Referring to FIG. 1, the present disclosure is a system 100 and method to mitigate precipitation clutter from received echoes in sea surveillance scenarios. First, the sea surface is compensated by sensitivity time control (STC), and then clutter data is extracted from consecutive scans. Then, data clustering is performed to find sea clutter-affected zones and required thresholds are computed for each zone and applied to reject clutter echoes. Here the sea surface from the reflected echo is suppressed by fitting the STC curve on data segregated using a coastline map. The method also removes outliers so as to avoid overestimation of sea clutter on STC due to echoes other than sea on the reflected signal. The property of sea spikes to vary from scan to scan is utilized to identify sea clutter-affected areas. The threshold computation is performed on the identified zones using statistical analysis of the echo powers in each zone to segregate sea spikes from targets.
[0022] In an embodiment, the system for mitigating precipitation clutter in an area of interest includes a radar 102 configured to receive a set of data pertaining to radar reflections in range, azimuth, resolution and pulse width of the area of interest. The area of interest is selected from marine surface target and any combination thereof. The system 100 is configured to reduce the impact of precipitation or cloud clutter on target detection and tracking by dynamically detecting and suppressing cloud clutter. The processor 104 can receive, from the radar, 102 radar reflections in range and azimuth of the area of interest. The radar reflections received are based on the range resolution and pulse width of the radar.
[0023] The processor 104 operatively coupled to the radar 102, the processor 104 configured to receive, from the radar, the set of data of the area of interest to quantize the received set of data. The processor 104 computes space-time clutter (STC) based on the quantized data by masking land clutter data, wherein the land clutter data pertains to reflections from buildings, mountains and any combination thereof. The processor 104 computes the STC using a higher-order polynomial. The processor 104 masks the land clutter data by employing a reference set of data, where the reference set of data pertains to a coastline map that serves as a reference to distinguish between land and sea areas within the radar coverage.
[0024] In another embodiment, the processor 104 can compute threshold values based on the range and azimuth of the received set of data. Interpolate the computed STC and the threshold values of the range and azimuth to suppress sea clutter data. The processor 104 coupled to a statistical analysis module that is performed on the echo data originating from the zones identified with the sea clutter data, facilitating the determination of the threshold values, which, upon subtraction, serves to suppress the sea clutter data and enhance target detection.
[0025] The processor 104 can compute STC based on quantised data of range and azimuth from received radar reflections, where the STC of land is masked using the stored map of the coastline in the area of interest. The combined computed STC and computed range azimuth-dependent threshold value are interpolated in range and azimuth to enable suppression of sea clutter. The sea clutter suppressed data is thresholded with a predefined threshold, and density is estimated, the zones where density is more than a predefined value are clustered using a data clustering algorithm.
[0026] The statistical analysis is performed on the echoes from zones detected with sea spikes (also referred to as sea clutter data) to arrive at an appropriate threshold which is subtracted to suppress sea spikes and enhance target detection. The sea spike and land suppressed data which is range-azimuth bin form is compressed in the azimuth domain using m out of n detection logic. The data clustering mechanism is applied to detect zones of echoes. The zones are tested for density, azimuth spread, range spread and symmetry.
[0027] The processor 104 can convert the sea clutter data and land cutter data to range-azimuth bin form and compress, by a detection logic, the range-azimuth bin form in azimuth domain. The processor 104 configured to organize processed echo data into rows, where each row represents data from a specific azimuth. Calculate a threshold of each row based on background noise. Consider the echo data in a certain number (N) of consecutive azimuths, compare the considered echo data to the threshold calculated based on the background noise and declare presence of the targets if a specified number (M) of consecutive azimuths exceeds the threshold of the background noise. The detection logic for data compression utilizes a specific logic configuration of M out of N, wherein M and N are dependent on sensor parameters pertaining to azimuth beam width.
[0028] The system 100, where the echo after sea surface and sea spike suppression is taken row by row, where each row consists of data from a specific azimuth. The data is then compared by a threshold calculated with respect to the noise floor. Data in n consecutive azimuths are taken and checked for amplitude higher than the noise threshold and if found more than m, the presence is declared. Where the choice of m and n is dependent on sensor parameters like azimuth beam width.
[0029] Further, apply a data clustering mechanism to the compressed data to detect zones of echo data to filter the zones aiding in distinguishing between targets and the undesired clutter. The processor is configured to detect each zone for density of echoes, spread in range and azimuth and symmetry of shape, wherein the symmetry of shape is tested by subtracting flipped version of the zones with respect to centre and calculating percentage of the area of interest not falling under the symmetry and determine presence of precipitation clutter in the zones based on the detection. Calculate the threshold for the zones where precipitation clutter is detected, wherein the threshold is determined by applying statistical analysis module to the echo data specifically within that zone and provide outline of the zones, which is identified as containing the precipitation clutter to enhance target tracking. The processor 104 provides the outline of the zones to a tracking module to enhance tracking of the targets.
[0030] The zones are obtained using the data clustering mechanism. Each zone is tested for the density of echoes present, spread in range and azimuth and symmetry of shape. The symmetry of the shape is tested by subtracting flipped version of the zone with respect to its centre and calculating the percentage of the area not falling under symmetry. Once the presence of precipitation clutter is established in the zone, the threshold is calculated for the zone by applying statistical analysis to the echo data from the zone. The outline of the zone is given out to be used by the tracking module for further improvement in target tracking.
[0031] Thus, the present invention overcomes the drawbacks, shortcomings, and limitations associated with existing solutions, and provides a system that reduces the impact of precipitation or cloud clutter on target detection that dynamically detects, tags clutter affected zones and suppresses cloud/precipitation clutter digitally without impacting detection capability of the surveillance system. The system detects clouds dynamically and gives out precise cloud contour with respect to the radar returns so as to assist the tracker to avoid the formation of false tracks by creating track inhibit zones without masking the true targets.
[0032] FIG. 2 illustrates an exemplary flow chart of a method for mitigating precipitation clutter in an area of interest, in accordance with an embodiment of the present disclosure.
[0033] Referring to FIG. 2, the method 200 includes at block 202, the radar configured to receive a set of data pertaining to radar reflections in range, azimuth, resolution and pulse width of the area of interest.
[0034] At block 204, the processor operatively coupled the radar, the processor configured to receive, from the radar, the set of data of the area of interest to quantize the received set of data.
[0035] At block 206, compute space-time clutter (STC) based on the quantized data by masking land clutter data, wherein the land clutter data pertains to reflections from buildings, mountains and any combination thereof. At block 208, compute threshold values based on the range and azimuth of the received set of data.
[0036] At block 210, interpolate the computed STC and the threshold values of the range and azimuth to suppress sea clutter data. At block 212, convert the sea clutter data and land cutter data to range-azimuth bin form. At block 214 compress, by a detection logic, the range-azimuth bin form in azimuth domain and at block 216, apply data clustering mechanism to the compressed data to detect zones of echo data to filter the zones aiding in distinguishing between targets and the undesired clutter.
[0037] It will be apparent to those skilled in the art that the system 100 of the disclosure may be provided using some or all of the mentioned features and components without departing from the scope of the present disclosure. While various embodiments of the present disclosure have been illustrated and described herein, it will be clear that the disclosure is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions, and equivalents will be apparent to those skilled in the art, without departing from the spirit and scope of the disclosure, as described in the claims.

ADVANTAGES OF THE PRESENT INVENTION
[0038] The present invention provides a system that reduces the impact of precipitation or cloud clutter on target detection.
[0039] The present invention provides a system that dynamically detects, tags clutter-affected zones and suppresses cloud/precipitation clutter digitally without impacting the detection capability of the surveillance system.
[0040] The present invention provides a system that detects clouds dynamically and gives out precise cloud contour with respect to the radar returns so as to assist the tracker to avoid the formation of false tracks by creating track-inhibit zones without masking the true targets.
,CLAIMS:1. A system (100) for mitigating precipitation clutter in an area of interest, comprising:
a radar (102) configured to receive a set of data pertaining to radar reflections in range, azimuth, resolution and pulse width of the area of interest;
a processor (104) operatively coupled to the radar, the processor configured to:
receive, from the radar, the set of data of the area of interest to quantize the received set of data;
compute space-time clutter (STC) based on the quantized data by masking land clutter data, wherein the land clutter data pertain to reflections from buildings, mountains and any combination thereof;
compute threshold values based on the range and azimuth of the received set of data;
interpolate the computed STC and the threshold values of the range and azimuth to suppress sea clutter data;
convert the suppressed sea clutter data and land cutter data to range-azimuth bin form;
compress, by a detection logic, the range-azimuth bin form in azimuth domain; and
apply data clustering mechanism to the compressed data to detect zones of echo data to filter the zones aiding in distinguishing between targets and undesired clutter.

2. The system of claim 1, wherein the processor (104) computes the STC using a higher-order polynomial.

3. The system of claim 1, wherein the processor (104) masks the land clutter data by employing a reference set of data, the reference set of data pertains to coastline map that serves as a reference to distinguish between land and sea areas within radar coverage.

4. The system of claim 1, wherein the processor (104) is coupled to a statistical analysis module that performs statistical analysis on the echo data originating from the zones identified with the sea clutter data, facilitating the determination of the threshold values, which, upon subtraction, serves to suppress the sea clutter data and enhance target detection.

5. The system of claim 1, wherein the processor (104) is configured to:
organize processed echo data into rows, where each row represents data from a specific azimuth;
calculate a threshold of each row based on background noise;
consider the echo data in a certain number (N) of consecutive azimuths;
compare the considered echo data to the threshold calculated based on the background noise; and
declare presence of the targets if a specified number (M) of consecutive azimuths exceeds the threshold of the background noise.

6. The system of claim 1, wherein the detection logic for data compression utilizes a specific logic configuration of M out of N, wherein M and N are dependent on sensor parameters pertaining to azimuth beam width.

7. The system of claim 1, wherein the processor (104) is configured to:
detect each zone for density of echoes, spread in range and azimuth and symmetry of shape, wherein the symmetry of shape is tested by subtracting flipped version of the zones with respect to centre and calculating percentage of the area of interest not falling under the symmetry;
determine presence of the precipitation clutter in the zones based on the detection;
calculate the threshold for detecting precipitation clutter in the zones, by applying the statistical analysis module to the echo data specifically within that zone; and
provide an outline of the zones, which are identified as containing the precipitation clutter to enhance the target tracking.

8. The system of claim 1, wherein the processor provides the outline of the zones to a tracking module to enhance tracking of the targets.

9. The system of claim 1, wherein the area of interest is selected from marine surface target and any combination thereof.

10. A method (200) for mitigating precipitation clutter in an area of interest, the method comprising:
receiving (202) a set of data pertaining to radar reflections in range, azimuth, resolution, and pulse width of the area of interest;
quantizing (204), at a processor, the received set of data;
computing (206), at the processor, space-time clutter (STC) based on the quantized data by masking land clutter data, wherein the land clutter data pertaining to reflections from buildings, mountains, and any combination thereof;
computing (208), at the processor, threshold values based on the range and azimuth of the received set of data;
interpolating (210), at the processor, the computed STC and threshold values in range and azimuth to suppress sea clutter data;
converting (212), at the processor, the suppressed sea clutter data and land clutter data to range-azimuth bin form;
compressing (214), at the processor, the range-azimuth bin form in azimuth domain using a detection logic; and
applying (216) a data clustering mechanism to the compressed data to detect zones of echo data, aiding in distinguishing between targets and undesired clutter.