基于多观测向量块稀疏的MIMO雷达非理想正交波形成像

doi:10.3969/j.issn.1001-506X.2020.12.10

Abstract

Abstract:

The multiple input multiple output (MIMO) radar waveform separation method based on the sparse recovery can improve the quality of non-ideal orthogonal waveform separation of MIMO radar and obtain high resolution images, which can replace the matched filter. However, due to the weak sparseness of the target image, the effect of the multiple measure ment vector (MMV) sparse recovery algorithm is limited. By adjusting the perceptual matrix to explore the block sparsity of the target image, a block sparse based on MMV sparse reconstruction algorithm is proposed to improve the image quality. Firstly, the improved composite trigonometric function (ICTF) is used as a smoothing function to approximate the l₀ norm. Then, it is extended to the MMV model based on block sparse. Finally, the robustness of the algorithm is improved by the adaptive adjustment of regularization parameters. The reconstruction performance of the algorithm under different sparseness and signal to noise ratio (SNR) is verified by experiments. The imaging effect of the MIMO radar on the multiple-scattering points target model is analyzed. The simulation results show that the proposed algorithm can improve the imaging quality.

Key words: multiple input multiple output (MIMO) imaging, multiple measurement vector (MMV), block sparse, improved composite trigonometric function (ICTF), robustness

CLC Number:

TN957.51

Qiao CHEN, Ningning TONG, Xiaowei HU, Shanshan DING. Non-ideal orthogonal waveforms imaging of MIMO radar based on multiple measurement vector block sparse algorithm[J]. Systems Engineering and Electronics, 2020, 42(12): 2747-2754.

Figures/Tables 12

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算法	姿态1		姿态2
算法	IC	IE	IC	IE
MF	2.280 7	8.978 9	2.186 2	9.072 1
MSL0	10.354 5	7.222 7	8.934 7	7.491 2
MBSSL0	10.594 2	7.120 2	9.142 4	7.392 4
MBICTF	10.654 8	7.045 4	9.293 9	7.300 6

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Non-ideal orthogonal waveforms imaging of MIMO radar based on multiple measurement vector block sparse algorithm

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