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28 March 2024 Sparse recovery space time adaptive processing based on log-determinant
Yuqing Chang, Xiaolin Du, Biao Jin, Tuanwei Tian, Jianbo Li
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Proceedings Volume 13091, Fifteenth International Conference on Signal Processing Systems (ICSPS 2023); 130910C (2024) https://doi.org/10.1117/12.3022952
Event: Fifteenth International Conference on Signal Processing Systems (ICSPS 2023), 2023, Xi’an, China
Abstract
Airborne radars usually face non-uniform clutter environments, and it is difficult to obtain enough independent and identical distributed (i.i.d.) training samples, which degrades the clutter suppression performance of space time adaptive processing (STAP). To address the problem, a log-determinant sparse recovery based STAP (LSR-STAP) method is proposed in this paper. Through the sparse recovery theory of low-rank matrices and the prior knowledge of clutter covariance matrices, the corresponding problem is modeled using the log-determinant (LogDet) approximation of the rank function, and the solution of the resulting nonconvex optimization problem is derived in the framework of the symmetric alternating direction method of multipliers (S-ADMM). The simulation results show the superiority of the proposed method over similar algorithms.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Yuqing Chang, Xiaolin Du, Biao Jin, Tuanwei Tian, and Jianbo Li "Sparse recovery space time adaptive processing based on log-determinant", Proc. SPIE 13091, Fifteenth International Conference on Signal Processing Systems (ICSPS 2023), 130910C (28 March 2024); https://doi.org/10.1117/12.3022952
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KEYWORDS
Clutter
Matrices
Covariance matrices
Radar
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