Subspace-Augmented Clutter Suppression Technique for STAP Radar

Wang, Zetao; Wang, Yongliang; Duan, Keqing; Xie, Wu

doi:10.1109/lgrs.2016.2519765

Cited by 46 publications

(29 citation statements)

References 14 publications

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“…Magraner et al [20] presented a new technique based on cellaveraging CFAR detection, and it achieved good detection results. Wang et al [21] proposed a novel subspace STAP algorithm by combining the conventional method and augmented subspace, and the numerical results demonstrated that the proposed algorithm has a superior performance in a finite-training-sample situation. Although much work has been done, the detection of slow or weak targets is still difficult.…”

Section: Introductionmentioning

confidence: 99%

Clutter Suppression and Target Tracking by the Low-Rank Representation for Airborne Maritime Surveillance Radar

et al. 2020

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Moving target detection is of vital importance to maritime security and maritime resource protection. However, the detection of slow or weak targets is difficult based on traditional methods. A new detection method is proposed by using the different motion variations of radar moving target and sea clutter in the range-Doppler spectrum sequence. The first step in implementing this method is the separation of moving target and sea clutter by the low-rank representation, in which the target and clutter are modeled as foreground and background components. Subsequently, a sea clutter discriminator is constructed within the sea clutter bandwidth to further remove the sea clutter (false alarms) that exists in the foreground. The proposed method can reduce the sea clutter power while maintaining the target power and improve the detection rate of moving targets, especially slow or weak targets. Data collected with airborne maritime surveillance radar in maritime moving target indication (MMTI) mode are used to validate the performance of the proposed method. The experimental results demonstrate that the improvement in the signal-to-clutter ratio (SCR) obtained with the proposed method is better than that obtained with space-time adaptive processing (STAP, including 1DT-STAP, 3DT-STAP and sparse-STAP) and principal component pursuit (PCP) methods; additionally, the figure of merit (FOM) of the proposed method is higher than that of the constant false alarm rate (CFAR) and PCP method. Furthermore, the tracks of ships are obtained by applying a location constraint to the foreground sequence.

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Section: Introductionmentioning

confidence: 99%

Clutter Suppression and Target Tracking by the Low-Rank Representation for Airborne Maritime Surveillance Radar

et al. 2020

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“…Subspace-based methods have also been studied in the literature by using the principal subspace of CNCM instead. For example, the eigen-canceller method based on the direct eigenvalue decomposition (EVD) of CNCM was developed in [7]. This method, however, is not attractive for real-time processing due to its high computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Random Nyström Approach to Clutter Subspace Estimation for Polarimetric Space-Time Adaptive Processing

et al. 2020

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A random Nyström (R-Nyström) scheme for clutter subspace estimation is proposed in the context of polarimetric space-time adaptive processing (pSTAP). Unlike the standard Nyström scheme making use of only partial columns of the clutter plus noise covariance matrix (CNCM), R-Nyström exploits full CNCM information with a properly designed selection procedure under the newly developed random ridge cross leverage score (RRCLS) criterion. With R-Nyström, sup-ported by the complete CNCM columns, upgraded clutter subspace estimation can be achieved at the expense of an insignificant increase in computational complexity, in contrast to the standard Nyström. The R-Nyström-based pSTAP, termed pR-Nyström, is shown to be superior over the current eigendecomposition-free subspace pSTAP in the signal to clutter plus noise loss and computational complexity. The efficacy of R-Nyström/pR-Nyström is validated by the simulation results.

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“…In nulled SR-STAP, the clutter subspace is first obtained by using SR algorithms, and then the STAP weight vector is calculated by projecting the data of CUT into the subspace orthogonal to the clutter subspace [35]. In [36], a subspace-augmented STAP algorithm is presented, which utilizes the direct portion and supplemented portion to construct the entire clutter subspace. In [37], a fast STAP based on the projection approximation subspace tracking (PAST) with sparse constraint and the low-rank property of CCM is developed, which uses a small training data support and can achieve a robust estimation of the clutter subspace.…”

Section: Introductionmentioning

confidence: 99%

Sparse Bayesian Learning Based Space-Time Adaptive Processing Against Unknown Mutual Coupling for Airborne Radar Using Middle Subarray

Guo

Zhang

et al. 2019

IEEE Access

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Sparse-recovery-based space-time adaptive processing (STAP) methods can exhibit superior clutter suppression performance with limited training data. However, the clutter suppression performance seriously degrades when the mutual coupling is present in the STAP array elements. In this paper, a sparse Bayesian learning (SBL)-based STAP method against the mutual coupling by using the middle subarray is proposed. Specifically, the mutual coupling matrix (MCM) of the STAP uniform linear array is approximately described as a banded symmetric Toeplitz matrix since the effect of mutual coupling between two array elements is inversely related to their distance and can be negligible when their distance is larger than a few wavelengths. Utilizing this specific structure of the MCM, a mutual coupling mitigation method is developed for STAP by rearranging the received snapshots with the designed spatial-temporal selection matrix. Then, to efficiently recover the clutter angle-Doppler profile, the modified fast converging SBL (FCSBL) named adaptive FCSBL algorithm and its multiple measurement vector case adaptive MFCSBL algorithm are developed with only partial hyper-parameters being updated in a single iteration. The proposed method not only achieves the superior clutter suppression performance in the presence of unknown mutual coupling, but also has low computational load. The simulation results are implemented to validate the effectiveness of the proposed methods. INDEX TERMS Space-time adaptive processing, mutual coupling matrix, spatial-temporal selection matrix, sparse Bayesian learning.

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Subspace-Augmented Clutter Suppression Technique for STAP Radar

Cited by 46 publications

References 14 publications

Clutter Suppression and Target Tracking by the Low-Rank Representation for Airborne Maritime Surveillance Radar

Clutter Suppression and Target Tracking by the Low-Rank Representation for Airborne Maritime Surveillance Radar

Random Nyström Approach to Clutter Subspace Estimation for Polarimetric Space-Time Adaptive Processing

Sparse Bayesian Learning Based Space-Time Adaptive Processing Against Unknown Mutual Coupling for Airborne Radar Using Middle Subarray

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