Sparsity-Based Direct Data Domain Space-Time Adaptive Processing with Intrinsic Clutter Motion

Yang, Zhaocheng; Qin, Yuliang; Lamare, Rodrigo C. de; Wang, Hongqiang; Li, Xiang

doi:10.1007/s00034-016-0301-z

Cited by 12 publications

(10 citation statements)

References 31 publications

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“…In this section, we present numerical simulation results to verify the theoretical derivation made above and compare the TCMR-STAP with the existing methods including the RCML-STAP [24], mDT-STAP [5], JDL-STAP [6], and SMI-STAP [2]. e parameters of the radar system are assumed as λ � 0.05m,…”

Section: Simulation Results and Discussionmentioning

confidence: 91%

“…e first category is the failure to take advantage of the prior knowledge to reduce sample size, which can be defined as the generalized reduced sample size method. For the dimension reduction [5][6][7] and reduced rank STAP [8], the sample size is twice the reduced dimension or clutter rank, but it is still large. e direct data domain method only uses test unit data to suppress clutter at the cost of freedom loss, which is only suitable for uniform linear array and planar array [9].…”

Section: Introductionmentioning

confidence: 99%

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STAP Based on Toeplitz Covariance Matrix Reconstruction for Airborne Radar

Liu

Zou

et al. 2020

Mathematical Problems in Engineering

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We consider the problem of clutter covariance matrix (CCM) estimation for space-time adaptive processing (STAP) radar in the small sample. In this paper, a fast efficient algorithm for CCM reconstruction is proposed to overcome this shortcoming for the linear structure. Particularly, we present a low-rank matrix recovery (LRMR) question about CCM estimation based on the Toeplitz structure of CCM and the prior knowledge of the noise. The closed-form solution is obtained by relaxing the nonconvex LRMR problem that the trace norm replaces the rank norm. The target can then be efficiently detected by using the recovered CCM according to the STAP theorem. We also analyze the algorithm model under the linear structure in the presence of unknown mutual coupling. It is shown that our method can obtain accurate CCM in the small sample, with even higher accuracy than the traditional algorithms in the same number of samples. It also can reduce the coupling effect and obtain more degrees of freedom (DOF) with limited sensors and pulses by utilizing sparse linear structure (SLS) and improve angle and Doppler resolutions. Finally, numerical simulations have verified the effectiveness of the proposed method in comparison with some of the existing methods.

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Section: Simulation Results and Discussionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

STAP Based on Toeplitz Covariance Matrix Reconstruction for Airborne Radar

Liu

Zou

et al. 2020

Mathematical Problems in Engineering

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“…Lately, Sparse representation technology has been widely considered in various fields [ 18 , 19 ], which encourages research on sparse-aware STAP. Sparse-aware STAP reconstructs the clutter covariance matrix by using sparse representation techniques, improving suppression capability and offering high-resolution imagery in a deficient-training-sample situation [ 20 , 21 , 22 ]. Assuming that the reference signal is pure, the aforementioned STAP algorithms can provide the desirable suppression performance in the airborne passive radar.…”

Section: Introductionmentioning

confidence: 99%

Clutter Suppression Method for Off-Grid Effects Mitigation in Airborne Passive Radars with Contaminated Reference Signals

Deng

Zhang

et al. 2021

Sensors

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For an airborne passive radar with contaminated reference signals, the clutter caused by multipath (MP) signals involved in the reference channel (MP clutter) corrupts the covariance estimation in space-time adaptive processing (STAP). In order to overcome the severe STAP performance degradation caused by impure reference signals and off-grid effects, a novel MP clutter suppression method based on local search is proposed for airborne passive radar. In the proposed method, the global dictionary is constructed based on the sparse measurement model of MP clutter, and the global atoms that are most relevant to the residual are selected. Then, the local dictionary is designed iteratively, and local searches are performed to match real MP clutter points. Finally, the off-grid effects are mitigated, and the MP clutter is suppressed from all matched atoms. A range of simulations is conducted in order to demonstrate the effectiveness of the proposed method.

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“…More recently, motivated by compressive sensing techniques, sparsity-based STAP has been applied to GMTD and its basic idea is to formulate the observing scene with the target and clutter [72]- [76], [78], [79], only the clutter [67], [79]- [87] or only the target [79], [88]- [90] estimation problem as a sparse recovery/representation (SR) problem or a low-rank matrix estimation problem [84]. Compared with conventional reduced-dimension and reduced-rank STAP algorithms, the sparsity-based STAP algorithms provide high-resolution of the scene and exhibit much better performance in a very small training support, or even in a single snapshot.…”

Section: Introductionmentioning

confidence: 99%

Sparsity-Based STAP Design Based on Alternating Direction Method with Gain/Phase Errors

Yang,

de Lamare,

Liu

2017

Preprint

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We present a novel sparsity-based space-time adaptive processing (STAP) technique based on the alternating direction method to overcome the severe performance degradation caused by array gain/phase (GP) errors. The proposed algorithm reformulates the STAP problem as a joint optimization problem of the spatio-Doppler profile and GP errors in both single and multiple snapshots, and introduces a target detector using the reconstructed spatio-Doppler profiles. Simulations are conducted to illustrate the benefits of the proposed algorithm.

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Sparsity-Based Direct Data Domain Space-Time Adaptive Processing with Intrinsic Clutter Motion

Cited by 12 publications

References 31 publications

STAP Based on Toeplitz Covariance Matrix Reconstruction for Airborne Radar

STAP Based on Toeplitz Covariance Matrix Reconstruction for Airborne Radar

Clutter Suppression Method for Off-Grid Effects Mitigation in Airborne Passive Radars with Contaminated Reference Signals

Sparsity-Based STAP Design Based on Alternating Direction Method with Gain/Phase Errors

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