Adaptive Clutter Suppression for Airborne Random Pulse Repetition Interval Radar Based on Compressed Sensing

Liu, Zhen; Wei, Xiaofeng; Li, Xiang

doi:10.2528/pier12022001

Cited by 24 publications

(23 citation statements)

References 23 publications

(42 reference statements)

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“…Some of the research exists on the clutter suppression of airborne radar [25][26][27]. Similarly, some adaptive filter algorithms were used for clutter cancellation in PBR [22].…”

Section: Introductionmentioning

confidence: 99%

Fpga Implementation of Space-Time Adaptive Processing (Stap) Algorithm for Target Detection in Passive Radars

Mahmood

Alam²,

Jamil³

et al. 2013

PIER C

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Abstract-Space-Time Adaptive Processing (STAP) algorithm has recently been used in Passive Bi-static Radars (PBR) because it removes the clutter and non-cooperative transmitter effectively making the target detection easy in harsh environments like air-ground. Realtime implementation of STAP is a very challenging task as it is computationally-intensive, time-critical and resource-hungry process. This paper focuses on the Field-Programmable Gate Array (FPGA) implementation of STAP algorithm for passive radar using FM radio as transmitter of opportunity. The signals of interest were collected using an eight-channel software-defined radar with a uniform circular array (UCA). The STAP processing was simulated using MATLAB and hardware implementation was carried out on a Xilinx Virtex-6 FPGA. The system is tested using experimental radar data. Timing and Power analysis of hardware implementation justifies that FPGA provides a fast and reliable platform for STAP real-time radar processing.

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“…Some of the research exists on the clutter suppression of airborne radar [25][26][27]. Similarly, some adaptive filter algorithms were used for clutter cancellation in PBR [22].…”

Section: Introductionmentioning

confidence: 99%

Fpga Implementation of Space-Time Adaptive Processing (Stap) Algorithm for Target Detection in Passive Radars

Mahmood

Alam²,

Jamil³

et al. 2013

PIER C

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“…A great deal of compressed sensing based methods have been applied to radar systems [8][9][10][11][12][13][14][15][16][17][18], which recover the target scene from fewer measurements than traditional methods. In [8], it is demonstrated that the compressed sensing can eliminate the need for matched filter at the receiver and has the potential to reduce the required sampling rate.…”

Section: Introductionmentioning

confidence: 99%

“…In [8], it is demonstrated that the compressed sensing can eliminate the need for matched filter at the receiver and has the potential to reduce the required sampling rate. [9] presents an adaptive clutter suppression method for airborne random pulse repetition interval radar by using prior knowledge of clutter boundary in Doppler spectrum. [10] focuses on monostatic chaotic multiple-input-multiple-output (MIMO) radar systems and analyze theoretically and numerically the performance of sparsity-exploiting algorithms for the parameter estimation of targets at Low-SNR.…”

Section: Introductionmentioning

confidence: 99%

Compressed Sensing Based Track Before Detect Algorithm for Airborne Radars

Liu¹,

Han²,

Yao³

et al. 2013

PIER

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Abstract-This paper presents a novel compressed sensing based track before detect (CS-TBD) algorithm. The proposed algorithm reconstructs the whole radar scenario (direction of arrival (DOA)-Doppler plane) for each range gate at consecutive scans using an improved stagewise orthogonal matching pursuit (StOMP) algorithm, resulting in a three-dimensional range-DOA-Doppler space. It then performs temporal tracking in the newly built three-dimensional range-DOA-Doppler space, based on the information from multiple illuminations during each scan, as well as among consecutive scans. In the proposed CS-TBD algorithm, the improved StOMP algorithm together with the temporal tracking, can effectively distinguish true targets from false targets and clutter based on information from multiple illuminations.

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“…So far, CS theorem has been widely used in the signal processing of SAR [16][17][18][19][20]. Lin et al applied CS to CSAR imaging, aiming to reduce the number of samples and provide high image quality [21].…”

Section: Introductionmentioning

confidence: 99%

Random Step Frequency Csar Imaging Based on Compressive Sensing

Yu¹,

Zhang²

2012

PIER C

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Abstract-Circular synthetic aperture radar (CSAR) imaging based on compressive sensing with random step frequency (RSF) as transmitted signal is introduced. CSAR is capable of obtaining both two-dimensional high resolution image and three-dimensional image due to a circular collection trajectory. RSF signal shares good characteristics of noise signals including "thumbtack-shape" ambiguity function, low probability of interception, and strong anti-jamming capability. As a result, CSAR adopting RSF signal can make use of advantages of both CSAR and RSF signal. Compressive sensing is a new data acquisition and reconstruction theorem for sparse or compressible signals, which needs fewer samples to reconstruct signals than traditional Nyquist theorem. Simulation results show that both two-dimensional and three-dimensional targets can be well reconstructed from few samples by applying compressive sensing to RSF CSAR imaging.

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Adaptive Clutter Suppression for Airborne Random Pulse Repetition Interval Radar Based on Compressed Sensing

Cited by 24 publications

References 23 publications

Fpga Implementation of Space-Time Adaptive Processing (Stap) Algorithm for Target Detection in Passive Radars

Fpga Implementation of Space-Time Adaptive Processing (Stap) Algorithm for Target Detection in Passive Radars

Compressed Sensing Based Track Before Detect Algorithm for Airborne Radars

Random Step Frequency Csar Imaging Based on Compressive Sensing

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