ISAR Imaging Method Based on the Bayesian Group-sparse Modeling

Wu Cheng-guang; Deng Bin; Su Wu-ge; Wang Hong-qiang; Qin Yu-liang

doi:10.11999/JEIT141624

Volume 37 Issue 12

Jan. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2015 > 37(12): 2941-2947

Wu Cheng-guang, Deng Bin, Su Wu-ge, Wang Hong-qiang, Qin Yu-liang. ISAR Imaging Method Based on the Bayesian Group-sparse Modeling[J]. Journal of Electronics & Information Technology, 2015, 37(12): 2941-2947. doi: 10.11999/JEIT141624

Citation:

Wu Cheng-guang, Deng Bin, Su Wu-ge, Wang Hong-qiang, Qin Yu-liang. ISAR Imaging Method Based on the Bayesian Group-sparse Modeling[J]. Journal of Electronics & Information Technology, 2015, 37(12): 2941-2947. doi: 10.11999/JEIT141624

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ISAR Imaging Method Based on the Bayesian Group-sparse Modeling

doi: 10.11999/JEIT141624

Funds:

The National Natural Science Foundation of China (61171133)

Received Date: 2014-12-18
Rev Recd Date: 2015-10-19
Publish Date: 2015-12-19

Abstract

Abstract

The traditional sparse ISAR imaging method mainly considers the recovery of coefficients on individual scatters. However, in the practice situation, the target scatters presented by blocks or groups do not emerge on individual. In this case, the usual sparse recover algorithm can not depict the shape of real target, thus, the group-sparse recover approaches are adopted to reconstruct the coefficients of target scatters. The recovery method based on the Bayesian Group-Sparse modeling and Variational inference (VBGS) uses a hierarchical construction of a general signal prior to model the group sparse signals and contain the merit of Sparse Bayesian Learning (SBL) on parameters learning, as a result, it can reconstruct the group sparse signal better than the usual recover algorithm. The VBGS method uses the variational Bayesian inference approach to estimate the parameters of the unknown signal automatically and does not require the parameter-tuning procedures. Considering the sparse group target, this paper combines the Compress Sensing (CS) theory and the VBGS method to reconstruct the ISAR image. The result of experiments show that the proposed method can improve the imaging accuracy compared with traditional algorithm, and can fit to reconstruct the image of ISAR target which has group structure.
- Inverse SAR (ISAR),
- Group sparse model,
- Compress Sensing (CS),
- Bayesian Group-Sparse modeling and Variational inference (VBGS)

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References(20)

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