基于稀疏贝叶斯学习的字典失配杂波空时谱估计方法

doi:10.7527/S1000-6893.2020.24592

Abstract

Abstract: Space-Time Adaptive Processing (STAP) via clutter spectrum sparse recovery can reduce the requirement for clutter sample size, and suppress the clutter of airborne radar effectively using limited training samples. The space-time plane is discretized into some grid points, and the space-time steering vector dictionary with discretized grid points is designed. However, the clutter ridge is not located exactly on the pre-discretized grid points in presence of dictionary mismatch. The dictionary mismatch effect degrades significantly the performance of STAP via sparse recovery. In this paper, a sparse Bayesian learning method for eliminating dictionary mismatch in clutter spectrum estimation is proposed. A dynamic dictionary is established by two-dimensional Taylor's series, and the dictionary mismatch biases are considered as parameters to be estimated by the sparse Bayesian learning method. The space-time steering dictionary is compensated by the estimated mismatch biases. The clutter-plus-noise covariance matrix is reconstructed with the compensated dictionary, and then the clutter space-time spectrum is calculated finally. Numerical results show that the proposed method can obtain better accuracy in clutter spectrum sparse recovery, and provide better performance of clutter suppression in comparison with the STAP using the sparse Bayesian learning method with the pre-discretized dictionary.

Key words: airborne array radar, space-time adaptive processing (STAP), sparse recovery, dictionary mismatch, sparse Bayesian learning

CLC Number:

V243.2

ZHANG Tao, ZHONG Lunlong, LAI Ran, GUO Juncheng. Sparse Bayesian learning method for eliminating dictionary mismatch in clutter space-time spectrum estimation[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(6): 324592-324592.

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Sparse Bayesian learning method for eliminating dictionary mismatch in clutter space-time spectrum estimation

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