基于稀疏贝叶斯学习的稳健STAP算法

doi:10.12305/j.issn.1001-506X.2023.10.05

Abstract

Abstract:

To improve the performance of sparse recovery space-time adaptive processing (SR-STAP) algorithms with both array gain/phase errors and grid mismatches, a sparse Bayesian learning-based robust SR-STAP approach is proposed in this paper. Firstly, the SR-STAP signal model with mismatched errors is constructed using the Kronecker structure of the space-time steering vector. Secondly, the angle-Doppler profile and mismatched parameters are alternatively achieved by utilizing the Bayesian inference and expectation-maximization algorithm. Finally, the precise clutter-plus-noise covariance matrix is estimated and the corresponding weight vector is calculated with the above obtained parameters. Simulation results verify that the proposed algorithm can significantly improve the target detection performance with mismatched sparse signal model.

Key words: space-time adaptive processing (STAP), array gain/phase errors, grid mismatches, sparse Bayesian learning

CLC Number:

TN957.73

Zhongyue LI, Tong WANG. Sparse Bayesian learning-based robust STAP algorithm[J]. Systems Engineering and Electronics, 2023, 45(10): 3032-3040.

Figures/Tables 8

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References 26

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参数	数值
阵元数	8
脉冲数	10
波长/m	0.3
带宽/MHz	3
脉冲重复频率/Hz	2 400
高度/m	6 000
平台速度/(m·s^-1)	162
杂噪比/dB	45

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Sparse Bayesian learning-based robust STAP algorithm

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