Claims:1. A system (100) for target detection, the system comprising:
an analog to digital converter (ADC) (102) that receives a set of signals to convent the received set of signals to digital set of signals;
a digital down-converter (DDC) (104) coupled to the ADC (102), the DDC (104) down converts the digital set of signals into a baseband signal;
a static random access memory (106) coupled to the DDC (104), the static random access memory stores the down converted data for each coherent time of radar;
a clutter removal module (108) coupled to the static random access memory, the clutter removal module removes the clutter present in the digital set of signals;
a doppler processing module (110) coupled to the clutter removal module, the doppler processing module converts time domain data into frequency domain data, the doppler processing module process each frequency data separately; and
a target detection module (112) coupled to the doppler processing module 110, the target detection module receives the data to classify the received data into a plurality of different sections,
wherein, the data of each sections is compared with the data of adjacent sections to detect discontinuity in the plurality of different sections, and
wherein, based on detection of the discontinuity in the plurality of different sections, the threshold value is computed, the threshold value is required to differentiate the target from clutter.
2. The system as claimed in claim 1, wherein a timing signal generation module (114) generates synchronization signals required for each of the DDC, static random access memory, clutter removal module, doppler processing module and target detection module.
3. The system as claimed in claim 1, wherein the target detection module comprises section formation module (120), section summation module (122), section comparison module (124), discontinuity module (126), and threshold calculation module (128).
4. The system as claimed in claim 3, wherein the section formation module (120) classifies the data into the plurality of different sections for better discrimination in clutter environments, wherein the plurality of different sections is formed based on the size of blanking cells to reduce the effect of target residues in threshold computation.
5. The system as claimed in claim 3, wherein the section summation module (122) calculates the summation of each of the different sections, based on individual groups data of each of the different sections, wherein the summation of each of the different sections is computed based on configuration of data size in each of the different sections.
6. The system as claimed in claim 3, wherein the section comparison (124) and discontinuity module (126) identify the characteristics of environment near to the target, wherein section comparison and discontinuity module works on closed-loop based on the data of the section summation module, wherein the section comparison and discontinuity module decides the quality of data to be used for threshold computation.
7. The system as claimed in claim 3, wherein the threshold calculation module (128) calculates the threshold value based on classification of data.
8. The system as claimed in claim 7, wherein the threshold calculation module (128) ignores presence of interfering returns in threshold computation to improve target detection in multi-target scenario.
9. The system as claimed in claim 1, wherein the system provides better detection for the target even in high ground clutter scenario having Rayleigh or equivalent distributions.
10. A method (700) for target detection, the method comprising:
receiving (702), at an analog to digital converter (ADC) a set of signals to convert the received set of signals to digital set of signals;
down-converting (704), at a digital down converter (DDC), the digital set of signals into baseband signal, the DDC coupled to the ADC;
storing (706), at a static random access memory, the down converted data for each coherent time of radar, the static random access memory coupled to the DDC;
removing (708), by a clutter removal module, the clutter present in the digital set of signals, the clutter removal module coupled to the static random access memory;
converting (710), at a doppler processing module, the time domain data into frequency domain data, the doppler processing module process each frequency data separately, the doppler processing module coupled to the clutter removal module; and
receiving (712), at a target detection module, the data to classify the received data into a plurality of different sections,
wherein, the data of each section is compared with the data of adjacent sections to detect discontinuity in the plurality of different sections, and
wherein, based on detection of the discontinuity in the plurality of different sections, the threshold value is computed, the threshold value is required to differentiate target from clutter.

, Description:TECHNICAL FIELD
[001] The present disclosure relates, in general, to radar systems and more specifically, relates to a system and method for target detection in clutter edge environment for radars.

BACKGROUND
[002] Current radar systems operate in several distinct modes, depending on the application. Few existing technologies in the field of radar systems may include radar signal multi-target detection method and device based on cell averaging constant false alarm rate (CA-CFAR). In this system, a two-step method to calculate an adaptive threshold is described. In the first step, target detection is carried out based on the CA-CFAR detection method. Whenever detecting a target, it is transferred to and executes step two that contains secondary detection module. This module, after removing the currently detected target, makes an uproar unit at the bottom to re-starts detection to all. However, the existing system suffers from the limitations of the lack of detection of the target when the target is in the proximity of clutter.
[003] Another existing system includes adaptive CFAR methods for detecting clutter edge radar target, steps are as follows: calculate separately with reference to the mean value of radar return data modulus value in sliding window A, with reference to the mean value of radar return data modulus value in sliding window B, with reference to the variance of radar return data modulus value in sliding window A and with reference to the variance of radar return data modulus value in sliding window B. It calculates separately with reference to the changeability parameter of radar return data modulus value, the mean value with reference to radar return data modulus value in the changeability parameter of radar return data modulus value in sliding window B and reference sliding window A and the ratio with reference to the mean value of radar return data modulus value in sliding window B in sliding window A. And then obtain the reference level of clutter in radar return data. Set CFAR detection threshold coefficient K0, then by the reference level and K of clutter in radar return is multiplied, if the radar return data modulus value in unit to be detected is greater than the obtained product value, shows in unit to be detected comprising radar target, otherwise there is no radar target in a unit to be detected.
[004] Although multiple systems exist today, these systems suffer from significant drawbacks. Therefore, there is a need in the art to provide a unique adaptive threshold calculation technique for detection of targets in clutter edge environment, and detects the target when the target is in proximity of the clutter.

OBJECTS OF THE PRESENT DISCLOSURE
[005] An object of the present disclosure relates, in general, to radar systems and more specifically, relates to a system and method for target detection in clutter edge environment for radars.
[006] Another object of the present disclosure provides unique adaptive threshold calculation technique for detection of targets in ground clutter edge region in which clutter amplitude is having Rayleigh distribution and power spectrum density obeys the Gaussian model, keeping probability of false alarms constant.
[007] Another object of the present disclosure provides a system that detects the target even when target is in proximity of clutter while maintaining the same processing loss as in case of CA-CFAR.
[008] Another object of the present disclosure provides a system that provides simple architecture to eliminate interfering targets participation in calculation of adaptive threshold for multiple interfering targets scenario to maintain constant probability of detection.
[009] Another object of the present disclosure provides a system that provides improved detection based on single stage, simple architecture to suit the environment even if it is having clutter or clear region.
[0010] Yet another object of the present disclosure provides a system that reduces the resource requirements of programmable device.

SUMMARY
[0011] The present disclosure relates, in general, to radar systems and more specifically, relates to a system and method for target detection in clutter edge environment for radars. The present disclosure describes a unique method of target detection for radars in a scenario, where the target and clutter are very close to each other. The present method distributes the data into different sections and compares the data of each section to the data of other sections to understand the environment near to the target. Based on discontinuity detected in different sections, it calculates the threshold adaptively to provide the best threshold value for the selected probability of detection and to minimize false alarms. The main advantage of the proposed method is improved detection based on a single-stage, simple architecture to suit the environment even if it is having clutter or a clear region.
[0012] In an aspect, the present disclosure provides a system for target detection, the system including an analog to digital converter (ADC) that receives a set of signals to convent the received set of signals to digital set of signals, a DDC coupled to the ADC, the DDC down-converts the digital set of signals into baseband signal, a static random access memory 106 coupled to the DDC 104, the static random access memory stores the down-converted data for each coherent time of radar, a clutter removal module coupled to the static random access memory, the clutter removal module removes the clutter present in the digital set of signals, a doppler processing module coupled to the clutter removal module, the doppler processing module converts time-domain data into frequency domain data, the doppler processing module process each frequency data separately; and a target detection module coupled to the doppler processing module, the target detection module receives the data to classify the received data into a plurality of different sections, wherein the data of each sections is compared with the data of adjacent sections to detect discontinuity in the plurality of different sections, and wherein based on detection of the discontinuity in the plurality of different sections, the threshold value is computed, the threshold value is required to differentiate the target from clutter.
[0013] In an embodiment, a timing signal generation module generates synchronization signals required for each of the DDC, static random access memory, clutter removal module, doppler processing module and target detection module.
[0014] In another embodiment, the target detection module comprises section formation module, section summation module, section comparison module, discontinuity module, and threshold calculation module.
[0015] In another embodiment, the section formation module classifies the data into the plurality of different sections for better discrimination in clutter environments, wherein the plurality of different sections is formed based on the size of blanking cells to reduce the effect of target residues in threshold computation.
[0016] In another embodiment, the section summation module calculates the summation of each of the different sections, based on individual groups data of each of the different sections, wherein the summation of each of the different sections is computed based on configuration of data size in each of the different sections.
[0017] In another embodiment, the section comparison and discontinuity module identify the characteristics of environment near to the target, wherein section comparison and discontinuity module works on closed-loop based on the data of the section summation module, wherein the section comparison and discontinuity module decides the quality of data to be used for threshold computation.
[0018] In another embodiment, the threshold calculation module calculates the threshold value based on classification of data.
[0019] In another embodiment, the threshold calculation module ignores presence of interfering returns in threshold computation to improve target detection in multi-target scenario.
[0020] In another embodiment, the system provides better detection for the target even in high ground clutter scenario having Rayleigh or equivalent distributions.
[0021] 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
[0022] 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.
[0023] FIG. 1A illustrates an exemplary representation of a system for target detection in clutter edge environment, in accordance with an embodiment of the present disclosure.
[0024] FIG. 1B illustrates an exemplary high-level block diagram of the system, in accordance with an embodiment of the present disclosure.
[0025] FIG. 2 illustrates an exemplary view of the proposed target detection module, in accordance with an embodiment of the present disclosure.
[0026] FIG. 3 illustrates an exemplary state diagram of the proposed method, in accordance with an embodiment of the present disclosure.
[0027] FIG. 4 illustrates section summation architecture in accordance with an embodiment of the present disclosure.
[0028] FIG. 5 illustrates the configuration of range cells around cell-under-test in accordance with an embodiment of the present disclosure.
[0029] FIGs. 6A-6B illustrate the simulated result of conventional approach CA-CFAR used to control false alarm as compared to proposed approach.
[0030] FIG. 7 illustrates an exemplary flow chart of a method for target detection in clutter edge environment, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION
[0031] 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.
[0032] 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.
[0033] The present disclosure relates, in general, to radar systems and more specifically, relates to a system and method for target detection in clutter edge environment for radars. The present disclosure describes a unique method of target detection for radars in a scenario, where the target and clutter are very close to each other. The present method distributes the data into different sections and compares the data of each section to the data of other sections to understand the environment near to the target. Based on discontinuity detected in different sections, it calculates the threshold adaptively to provide the best threshold value for the selected probability of detection and to minimize false alarms. The main advantage of the proposed method is improved detection based on a single-stage, simple architecture to suit the environment even if it is having clutter or a clear region. The present disclosure can be described in enabling detail in the following examples, which may represent more than one embodiment of the present disclosure.
[0034] FIG. 1A illustrates an exemplary representation of a system for target detection in clutter edge environment, in accordance with an embodiment of the present disclosure.
[0035] Referring to FIG. 1A, radar system 100 (also referred to as system 100, herein) may be configured for target detection in a clutter edge environment and can be implemented for pulsed or continuous wave radar. The processor for implementing the proposed architecture can be any programmable device, which has sufficient resources to implement the proposed method. The system 100 may include analog to digital converter (ADC) 102 also interchangeable referred to as ADC data capturing module 102, digital down converter (DDC) 104, static random access memory (SRAM) 106, clutter removal module 108, doppler processing module 110, target detection module 112, and timing signal generation module 114. System 100 can detect a target for radar in the clutter edge region, keeping the probability of false alarms constant and without degrading the probability of detection. System 100 can adapt to the environment and calculate the threshold in one stage only, even in the case of a non-homogenous environment, due to the parallel architecture of different modules.
[0036] In an embodiment, the ADC 102 configured to receive a set of signals, the set of signals pertaining to radar returns signals with carrier frequency signals. The ADC 102 adapted to convent the received set of signals to a digital set of signals. The DDC 104 coupled to the ADC 102, the DDC 104 configured to down-convert the digital set of signals also interchangeably referred to as input signal to a baseband signal. The static random access memory 106 coupled to the DDC 104, the static random access memory 106 configured to store the down-converted data for each coherent time of radar processing. The clutter removal module 108 coupled to the static random access memory 106, the clutter removal module 108 configured to remove the clutter or static returns present in the digital set of signals also interchangeably referred to as return signals.
[0037] In another embodiment, the Doppler processing module 110 coupled to the clutter removal module 108, the doppler processing module 110 configured to convert time-domain data into frequency domain and process each band-limited frequency data separately. The target detection module 112 coupled to the doppler processing module 110, the target detection module 112 configured to calculate the threshold adaptively and based on detection the module 112 declares the radar returns as the target or no target. The timing signal generation module 114 configured to generate all the synchronization signals required for each module such as DDC 102, static random access memory 106, clutter removal module 108, doppler processing module 110, and target detection module 112.
[0038] In another embodiment, the target detection module 112 can receive the data from the doppler processing module 110 to classify the received data into different sections, where the data of each sections can be compared with the data of adjacent sections to detect discontinuity in the different sections. Based on detection of the discontinuity in the different sections, the threshold value can be computed, where the threshold value is required to differentiate the target from clutter.
[0039] FIG. 1B illustrates an exemplary high-level block diagram of the system, in accordance with an embodiment of the present disclosure. As shown in FIG. 1B, the in-phase (I) and quadrature-phase (Q) data coming from the receiver of radar acts as input to the system 100, where a threshold flag can be generated to differentiate the target from the clutter. The proposed method can be implemented in any programmable signal processor for radar.
[0040] In an embodiment, a filter bank 116 can process the incoming data and convert the time-domain signal to the frequency domain, whose resolution depends on radar pulse repetition time and number of coherent pulses. These two-dimensional data (range, doppler) is then passed to magnitude calculator 118, which can calculate the magnitude of each range bin for noise and return both separated in the frequency domain.
range bin power (n)=??(??)2+??(??)2
[0041] In another embodiment, the target detection module 112 can include section formation module 120, section summation module 122, section comparison module 124 and discontinuity module 126, and threshold calculation module 128. The section formation module 120 can receive the data from magnitude calculator 118 and divide the data into different sections for better discrimination in clutter environments.
[0042] For example, total six sections A, B, C, D, E and F, each of 12 range bins are formed by analysing the content of a 36-range bin window moving with range cell placed before and after the cell-under-test (CUT). A blanking cell is in any case left around it, whose extension is equal to a number of range bins sufficient to assure that a possible target falling inside the cell-under-test will not participate, with its echo, to the computation of the threshold against which the target itself will be compared. The size of the blanking cell can be varied between 2 to 4 range cells.
[0043] In another embodiment, the section summation module 122 can calculate the summation of each section, based on individual groups data. The section comparison module 124 and discontinuity module 126 can identify the characteristics of environment near to the target and threshold calculation module 128 can calculate the threshold based on data samples near to the target and classification of data to differentiate the target from clutter.
[0044] The embodiments of the present disclosure described above provide several advantages. One or more of the embodiments provide the unique adaptive threshold calculation technique for detection of targets in ground clutter edge region in which clutter amplitude is having Rayleigh distribution and power spectrum density obeys the Gaussian model, keeping probability of false alarms constant. The Rayleigh distribution is a continuous probability distribution for nonnegative-valued random variable and power spectrum density describes the analysis of the distribution of power over the entire frequency range. The Gaussian model are speci?c to passive clouds in which the dispersed molecules are assumed to be distributed with standard deviations depending on atmospheric conditions. The system 100 can detect the target even when the target is in proximity of the clutter while maintaining the same processing loss as in case of CA-CFAR. The system 100 can provide simple architecture to eliminate interfering targets participation in calculation of adaptive threshold for multiple interfering targets scenario to maintain constant probability of detection and system 100 can reduce the resource requirements of programmable device.
[0045] FIG. 2 illustrates an exemplary view of the proposed target detection module, in accordance with an embodiment of the present disclosure.
[0046] Referring to FIG. 2, the target detection module 112 can include section formation module 120, section summation module 122, section comparison module 124, discontinuity module 126, and threshold calculation module 128. The section formation module 120 can receive the data from the magnitude calculator 118 and divide the data into different sections for better discrimination in clutter environments. The section summation module 122 can calculate summation of each section, based on individual groups data. The section comparison 124 and discontinuity module 126 can identify the characteristics of environment near to target and threshold calculation module 128 can calculate the threshold based on data samples near to target and classification of data.
[0047] In an embodiment, the section formation module 120 is part of target detection module 112 and is dynamic in nature and form the section based on the size of blanking cells to reduce the effect of target residues in threshold computation. The section summation module 122 is a part of target detection module 112 and is adaptive in nature, which can compute the summation of each section based on the configuration of data size in each section. The section comparison 124 and discontinuity module 126 is part of target detection module 112, the section comparison 124 and discontinuity module 126 works on a closed loop and decides the quality of data to be used for threshold computation, which can be optimized in view of the required application. The threshold calculation module 128 is part of target detection module 112 and it adaptively ignores the presence of interfering returns in threshold computation to improve target detection in a multi-target scenario.
[0048] In another embodiment, the section formation module 120 can receive data and divided them into different sections, for example, a total of six sections A, B, C, D, E and F. Each section is further sub-divided into five groups as illustrated in FIG.4, where the section summation module 122 can calculate the summation of each section, based on individual groups. To minimise the effect of clutter edge on the calculation of adaptive threshold, section comparison 124 and discontinuity module 126 works on the closed-loop, based on the data of the section summation module 122. These modules decide whether to use all six sections A, B, C, D, E, F data or only a part of them for threshold calculation, depending on the characteristics concerning the clutter that is being examined.
[0049] In an exemplary implementation, first the two innermost sections i.e., sections C and section D, placed on the opposite site of the cell-under-test, are compared as illustrated in FIG. 5. If a discontinuity is found in the signal being analysed, that is the content of one section is much higher or much lower than the content of the other one, then for the calculation of the adaptive threshold only the section with the higher partial sum can be used. This means that the cell under test is affected by either a meteorological phenomenon e.g., rain or by an extended clutter area, which requires the utilization of a higher threshold in order to privilege the false alarm control with respect to detection.
[0050] If the content of sections C and D is equivalent, each one of the internal sections is compared with the associated adjacent intermediate section that is C with B and D with E. If either one of the two comparisons shows that a discontinuity is present, then for the calculation of the adaptive threshold only the two internal sections C and D are considered. This case can indicate that either the cell-under-test is not affected by meteorological clutter or jamming, and therefore it is useless to utilize a too-high threshold which would negatively affect the target inside the cell-under-test or the cell-under-test is inside a homogeneous phenomenon due to meteorological clutter or extended jamming, and consequently sections C and D must be considered in the calculation of the adaptive threshold in order to eliminate said phenomenon.
[0051] If from the comparisons between sections C and B and sections D and E, no big differences are evident the comparisons are continued considering the more external sections A and F as well. Also, in this case, if at least one of the two comparisons shows a discontinuity, the threshold calculation is carried-out considering only the four innermost sections, that is B, C, D and E. Otherwise the threshold is calculated considering all six sections A, B, C, D, E and F.
[0052] Threshold calculation module 128 can calculate the threshold based on decision of the section comparison 124 and discontinuity module 126. The threshold calculation is done as follows:
[0053] Step 1: If there is a discontinuity between section C and D then the section with higher partial sum multiplied by a factor selectable from operator, based on desired false alarm and probability of detection is used as the threshold.
[0054] Step 2: If section C and D are uniform and there is a discontinuity between (C and B) or (D and E) then average of sections (C and D), multiplied by a factor selectable from operator is used as threshold.
[0055] Step 3: If sections B, C, D and E are uniform and there is discontinuity between (B and A) or (E and F) then average of B, C, D and E, multiplied by a factor selectable from operator is used as threshold.
[0056] Step 4: If all the sections are uniform then the average of all the sections multiplied by a factor selectable from operator is used as threshold.
[0057] FIG. 3 illustrates an exemplary state diagram 300 of the proposed method, in accordance with an embodiment of the present disclosure. All the stages in state diagram are controlled by timing signals and if correct data is not available for the present state then control can either go to the previous state or it may wait in the current state.
[0058] FIG. 4 illustrates a section summation architecture 400 in accordance with an embodiment of the present disclosure. As shown in FIG. 4, each section is further sub-divided into five groups. The section summation module 122 can calculate summation of each section, based on individual groups as follows:
Sum1=
Sum2=
Sum3=
Sum4=
Sum5=
Section Sum= Sum1+Sum3+Sum5-max (sum1, sum 2, sum 3, sum 4, sum 5). This can eliminate the effect of any target, if present, in window during threshold calculation. Since the group with largest sum where there is influence of the target is subtracted.
[0059] FIG. 5 illustrates the configuration of range cells around cell-under-test 500 in accordance with an embodiment of the present disclosure. As shown in FIG. 5, a total of six sections A, B, C, D, E and F, each of 12 range bins are formed by analysing the content of a 36-range bin window moving with range cell placed before and after the cell-under-test (CUT). The blanking cell is in any case left around it, whose extension is equal to a number of range bins sufficient to assure that a possible target falling inside the cell-under-test will not participate, with its echo, to the computation of the threshold against, which the target itself will be compared. The size of the blanking cell can be varied between 2 to 4 range cells.
[0060] FIGs. 6A-6B illustrate the simulated result of conventional approach (CA-CFAR) used to control false alarm as compared to proposed approach. The target is simulated for signal to clutter ratio of -10dB. In case 1, as depicted in FIG 6A, when the target is not very close to clutter both conventional adaptive threshold method CA-CFAR and proposed method are able to detect the target. But in case 2, as depicted in FIG. 6B, where the target is in the proximity of clutter, the proposed method changes the threshold adaptively to detect the target but CA-CFAR fails to detect the target. Through various scenario condition evaluation, it is found that probability of detection is maintained at 90% for the proposed method even in the presence of high clutter.
[0061] Thus, there has been described an improved system and method to detect target for radar in clutter edge region, keeping probability of false alarms constant and without degrading the probability of detection. The proposed method adapts to the environment and calculate the threshold in one stage only, even in case of non-homogenous environment, due to parallel architecture of different modules.
[0062] FIG. 7 illustrates an exemplary flow chart of a method for target detection in clutter edge environment, in accordance with an embodiment of the present disclosure.
[0063] Referring to FIG 7, at step 702, an analog to digital converter (ADC) receives a set of signals to convent the received set of signals to digital set of signals. At block 704, a DDC coupled to the ADC, the DDC down converts the digital set of signals into baseband signal. At block 706, a static random access memory coupled to the DDC, the static random access memory stores the down converted data for each coherent time of radar. At block 708, a clutter removal module coupled to the static random access memory to remove the clutter present in the digital set of signals.
[0064] At block 710, a doppler processing module coupled to the clutter removal module 108, to convert time domain data into frequency domain data, the doppler processing module process each frequency data separately. At block 712, a target detection module coupled to the doppler processing module, the target detection module receives the data to classify the received data into a plurality of different sections, where the data of each sections is compared with the data of adjacent sections to detect discontinuity in the plurality of different sections, and where, based on detection of the discontinuity in the plurality of different sections, the threshold value is computed, the threshold value is required to differentiate the target from clutter.
[0065] 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 scope of the disclosure, as described in the claims.

ADVANTAGES OF THE PRESENT DISCLOSURE
[0066] The present disclosure provides unique adaptive threshold calculation technique for detection of targets in ground clutter edge region in which clutter amplitude is having Rayleigh distribution and power spectrum density obeys the Gaussian model, keeping probability of false alarms constant.
[0067] The present disclosure provides a system that detects the target even when target is in close proximity of clutter while maintaining the same processing loss as in case CA-CFAR.
[0068] The present disclosure provides a system that provides simple architecture to eliminate interfering targets participation in calculation of adaptive threshold for multiple interfering targets scenario to maintain constant probability of detection.
[0069] The present disclosure provides a system that reduces the resource requirements of programmable device.
[0070] The present disclosure provides a system that provides improved detection based on single stage, simple architecture to suit the environment even if it is having clutter or clear region.