基于协方差拟合准则的降维空时自适应处理方法

doi:10.12305/j.issn.1001-506X.2022.01.12

Abstract

Abstract:

The optimal space-time adaptive processing method is impractical due to the heavy computational complexity and the training samples required for estimating the clutter covariance matrix. To solve this problem, a reduced-dimension space-time adaptive processing method based on the covariance fitting criterion is proposed in this paper. The temporal filtering is firstly performed on the echo signals from each array element. Then, an optimization problem, which is asymptotically equivalent to the maximum likelihood estimator under Gaussian sources, is established by utilizing the covariance fitting criterion. Meanwhile, the covariance fitting optimization problem can be reformulated as a semi-definite problem, which can be solved by the CVX toolbox to estimate the clutter plus noise covariance matrix. Simulation experiments show that the proposed method requires fewer samples and has better clutter suppression performance compared with the combined space-time processing methods after Doppler filtering.

Key words: covariance fitting criterion, space-time adaptive processing, clutter suppression, reduced-dimension

CLC Number:

TN953

Xiaojiao PANG, Yongbo ZHAO, Chenghu CAO, Yili HU, Sheng CHEN. Reduced-dimension space-time adaptive processing method based on the covariance fitting criterion[J]. Systems Engineering and Electronics, 2022, 44(1): 86-93.

Figures/Tables 8

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Reduced-dimension space-time adaptive processing method based on the covariance fitting criterion

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