基于结构化字典学习的判别稀疏微波成像方法

doi:10.12305/j.issn.1001-506X.2025.01.10

Abstract

Abstract:

When dealing with multi-class targets, the sparse microwave imaging method of synthetic aperture radar (SAR) based on dictionary learning has redundant information in the dictionary, which leads to a decrease in imaging accuracy. To address this problem, a discriminative sparse microwave imaging method based on structured dictionary learning (SDL) is proposed. Firstly, using SDL to train for multi-class targets, a structured dictionary containing multiple sub-dictionaries is obtained, with each sub dictionary corresponding to a specific category of targets. Secondly, a discriminative sparse microwave imaging model is constructed by combining structured dictionaries, and the representation errors of the target are distinguished based on different sub-dictionaries during the processing. Finally, based on the discrimination results, the corresponding category sub-dictionary is selected for imaging. The experimental results show that compared with existing imaging methods, the proposed algorithm can better suppress artifact blurring and improve imaging accuracy under downsampling conditions.

Key words: structured dictionary learning (SDL), synthetic aperture radar (SAR), sparse microwave imaging, sparse representation

CLC Number:

TN957.52

Yang MENG, Guoru ZHOU, Jie LI, Bingchen ZHANG. Discriminative sparse microwave imaging method based on structured dictionary learning[J]. Systems Engineering and Electronics, 2025, 47(1): 94-100.

Figures/Tables 5

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Table 1

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Table 2

References 30

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仿真场景	采样率/%	匹配滤波	KSVD	本文算法
仿真场景	采样率/%	PSNR/SSIM	PSNR/SSIM	PSNR/SSIM
1	30	8.98/0.09	18.87/0.84	20.03/0.87
	50	13.39/0.65	18.92/0.91	20.31/0.91
	70	19.61/0.85	23.24/0.94	25.16/0.97
2	30	8.79/0.18	17.20/0.72	18.99/0.76
2	50	15.58/0.57	20.67/0.87	25.24/0.91
70	19.81/0.72	22.71/0.90	25.93/0.94
3	30	8.48/0.11	18.87/0.73	20.65/0.77
	50	15.99/0.57	20.07/0.86	24.17/0.88
	70	20.54/0.71	23.21/0.90	25.72/0.94
4	30	8.85/0.25	16.29/0.64	19.81/0.67
	50	15.59/0.46	20.33/0.73	23.80/0.75
	70	20.29/0.63	23.32/0.76	25.87/0.85

成像目标	采样率/%	匹配滤波	KSVD	本文算法
成像目标	采样率/%	PSNR/SSIM	PSNR/SSIM	PSNR/SSIM
BMP2	70	22.26/0.75	28.13/0.85	28.78/0.86
BMP2	50	14.61/0.51	22.68/0.68	24.88/0.72
BTR70	70	20.13/0.79	24.73/0.86	25.42/0.86
BTR70	50	14.68/0.61	21.13/0.70	22.26/0.72
T72	70	20.68/0.62	27.82/0.81	28.57/0.83
T72	50	14.43/0.46	22.15/0.64	24.98/0.73

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Discriminative sparse microwave imaging method based on structured dictionary learning

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