基于聚类求解TVAR模型的目标微多普勒分析

doi:10.12305/j.issn.1001-506X.2023.03.06

Abstract

Abstract:

Aiming at the problem of insufficient time-frequency resolution of existing time-frequency analysis methods for micro-Doppler analysis of targets, a time-frequency analysis algorithm based on clustering prior to solve the forward and backward time-varying autoregressive (TVAR) models is proposed for micro-Doppler analysis of spatial cone targets. Using an improved algorithm based on extended block sparse Bayesian learning (EBSBL), the time-invariant block sparse coefficient of the TVAR model adopts a prior of the clustering structure, and the correlation between adjacent sparse cofficient is also promoted by appropriate handling of the hyperparameters of the neighborhood. The correlation between adjacent sparse coefficients is combined with the known prior information of the time-invariant block sparse coefficient's block boundaries of the rigid target to solve the time-invariant coefficients. The experimental results of electromagnetic simulation and measured data show that the proposed algorithm in this paper can obtain a higher time-frequency resolution, with a higher time-frequency aggregation, and a stronger anti-noise performance than traditional methods in micro-Doppler analysis.

Key words: micro-Doppler analysis, forward and backward time-varying autoregressive (TVAR) model, block sparsity, cluster-structured prior

CLC Number:

TN957

Xiaofei LU, Shuojing JIN, Ling HONG, Fengzhou DAI. Target micro-Doppler analysis of TVAR model based on clustering[J]. Systems Engineering and Electronics, 2023, 45(3): 660-668.

Figures/Tables 7

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References 31

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距离单元	算法
距离单元	STFT	BSBL求解 BS-FBTVAR模型	本文算法
锥顶	8.687 9	9.516 2	5.081 6
中部	8.997 2	3.893 3	2.580 5
锥底	9.128 4	3.704 4	2.911 5

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Target micro-Doppler analysis of TVAR model based on clustering

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