基于TSVD的块稀疏重构雷达前视超分辨成像

doi:10.12305/j.issn.1001-506X.2023.07.15

Abstract

Abstract:

In response to the problem of traditional bayesian deconvolution methods failing to effectively utilize the block structure information of the area targets, and resulting in poor imaging performance for area targets. we apply the pattern-coupled sparse Bayesian learning (PCSBL) method to forward-looking imaging in this paper. Moreover, in response to the problem of rapid degradation of resolution in this method at low signal to noise ratio(SNR), we propose to use the truncated singular value decomposition (TSVD) method as the preprocessing before deconvolution, which can effectively suppress the noise amplification in the deconvolution process by discarding the small singular values of convolution matrix. Simulation results show that the proposed method still has good super-resolution imaging effect under low SNR.

Key words: forward-looking imaging, deconvolution, block sparse reconstruction, low signal to noise ratio (SNR), super resolution

CLC Number:

TN958.4

Zhengyi ZHAO, Yingni HOU. Radar forward-looking super resolution imaging based on block sparse reconstruction with TSVD[J]. Systems Engineering and Electronics, 2023, 45(7): 2051-2059.

Figures/Tables 12

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References 23

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参数	取值
载频/GHz	10
信号带宽/MHz	40
脉冲重复频率/Hz	1 500
主瓣波束宽度/(°)	3.6
天线扫描角度/(°)	-10~10
天线扫描速度/((°)/s)	60

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Radar forward-looking super resolution imaging based on block sparse reconstruction with TSVD

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