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

Li, Zhihui; Guo, Yiduo; Zhang, Yongshun; Zhou, Hao

doi:10.1109/access.2018.2889474

Cited by 8 publications

(7 citation statements)

References 54 publications

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“…In 8, only the clutter sparsity is exploited, where the sparsity of the STAP filter weight vector w represents the clutter sparsity. In addition, the other methods also exploit only the clutter sparsity [12]- [18].…”

Section: The Proposed Joint Sparse Stap Methodsmentioning

confidence: 99%

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A Joint Sparse Space-time Adaptive Processing Method

Xia

et al. 2024

IEEE Access

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At present, most of the sparse space-time adaptive processing(STAP) methods focus on exploiting the clutter sparsity. In this paper, different from the present sparse STAP methods, both the clutter sparsity and the target sparsity in STAP are considered at the same time, and a novel joint sparse STAP method is proposed. The proposed method imposes a sparse regularization on the clutter and the target to the minimum Capon Spectrum criterion. The processing results of the measured data shows that the output SCNR of the proposed method is 3dB higher than the method in [20] and 2dB higher than the method in [21].

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Section: The Proposed Joint Sparse Stap Methodsmentioning

confidence: 99%

“…Recently, motivated by the sparsity of clutter, many sparse STAP methods have been proposed [12]- [21], and they can be divided into two categories: the direct method [12]- [18] and the indirect method [19]- [21]. The direct method exploits directly the clutter sparsity to reconstruct the lowrank clutter covariance matrix (CCM) in STAP.…”

Section: Introductionmentioning

confidence: 99%

A Joint Sparse Space-time Adaptive Processing Method

Xia

et al. 2024

IEEE Access

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“…A sparsity-based STAP method with array gain/phase (GP) error self-calibration has been developed in [ 48 ], which iteratively solves an SR problem and an LS calibration problem. In [ 49 ], utilizing the specific structure of the mutual coupling matrix, a mutual coupling calibration method is developed for SBL-STAP by rearranging the received snapshots with the designed spatial-temporal selection matrix. In [ 50 ], under the framework of the alternating direction method (ADM), a constraint is added to the array GP errors, and the conventional sparsity-based STAP problem is transformed into a joint optimization problem of the angle-Doppler profile and the array GP errors.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage STAP Method Based on Fine Doppler Localization and Sparse Bayesian Learning in the Presence of Arbitrary Array Errors

Liu

Wang

et al. 2021

Sensors

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In the presence of unknown array errors, sparse recovery based space-time adaptive processing (SR-STAP) methods usually directly use the ideal spatial steering vectors without array errors to construct the space-time dictionary; thus, the steering vector mismatch between the dictionary and clutter data will cause a severe performance degradation of SR-STAP methods. To solve this problem, in this paper, we propose a two-stage SR-STAP method for suppressing nonhomogeneous clutter in the presence of arbitrary array errors. In the first stage, utilizing the spatial-temporal coupling property of the ground clutter, a set of spatial steering vectors with array errors are well estimated by fine Doppler localization. In the second stage, firstly, in order to solve the model mismatch problem caused by array errors, we directly use these spatial steering vectors obtained in the first stage to construct the space-time dictionary, and then, the constructed dictionary and multiple measurement vectors sparse Bayesian learning (MSBL) algorithm are combined for space-time adaptive processing (STAP). The proposed SR-STAP method can exhibit superior clutter suppression performance and target detection performance in the presence of arbitrary array errors. Simulation results validate the effectiveness of the proposed method.

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“…Therefore, the MCM of uniform linear array (ULA) is a banded Toeplitz structure [6]. Various adaptive calibration methods based on MCM are proposed [7]- [15]. In [7], the theory of characteristic modes is used to characterize the mutual coupling effect, and a compensation matrix is constructed to compensate the mutual coupling effect.…”

Section: Introductionmentioning

confidence: 99%

“…In [14], by taking advantage of the banded symmetric Toeplitz structure of MCM and subspace, the closed form mutual coupling coefficients are estimated, and a robust adaptive beamformer can be formed by the reconstructed interference plus noise covariance matrix. A STAP method based on sparse bayesian learning is proposed by utilizing the middle subarray [15]. Although advanced progress has been made in estimating MCM, it is still difficult to estimate accurately MCM in the heterogeneous environments.…”

Section: Introductionmentioning

confidence: 99%

Robust STAP With Reduced Mutual Coupling and Enhanced DOF Based on Super Nested Sampling Structure

2019

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In practical airborne radar, mutual coupling between array elements usually leads to spacetime steering vector distortion for space-time adaptive processing (STAP), which seriously degrades radar performance. In this paper, a robust STAP algorithm coupling the second order super nested arrays with pulses (2OSNAP) sampling structure is developed. The proposed method applies difference operation to construct virtual space-time snapshots in the virtual domain. To solve the clutter plus noise covariance matrix (CNCM) corresponding to the virtual space-time snapshots, the space-time smoothing method is used in this paper. Furthermore, the proposed method can directly give a set of virtual and robust STAP weight vector, which is convenient for the subsequent signal processing. Extensive numerical experiments were performed to verify the effectiveness and superiority of the proposed method. The test results also show that our proposed method provides more degrees of freedom (DOF), and eliminates the mutual coupling effect between array elements compared to most existing methods. INDEX TERMS Clutter suppression, degrees of freedom, mutual coupling, space-time adaptive processing, space-time super nested sampling.

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

Cited by 8 publications

References 54 publications

A Joint Sparse Space-time Adaptive Processing Method

A Joint Sparse Space-time Adaptive Processing Method

A Two-Stage STAP Method Based on Fine Doppler Localization and Sparse Bayesian Learning in the Presence of Arbitrary Array Errors

Robust STAP With Reduced Mutual Coupling and Enhanced DOF Based on Super Nested Sampling Structure

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