Feature Reconstruction of High Resolution SAR Imagery Based on Low Rank Matrix Completion

YANG Lei; WANG Tengteng; CHEN Yingjie; GAI Minghui; XU Hanwen

doi:10.11999/JEIT220992

Volume 45 Issue 8

Aug. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(8): 2965-2974

YANG Lei, WANG Tengteng, CHEN Yingjie, GAI Minghui, XU Hanwen. Feature Reconstruction of High Resolution SAR Imagery Based on Low Rank Matrix Completion[J]. Journal of Electronics & Information Technology, 2023, 45(8): 2965-2974. doi: 10.11999/JEIT220992

Citation:

YANG Lei, WANG Tengteng, CHEN Yingjie, GAI Minghui, XU Hanwen. Feature Reconstruction of High Resolution SAR Imagery Based on Low Rank Matrix Completion[J]. Journal of Electronics & Information Technology, 2023, 45(8): 2965-2974. doi: 10.11999/JEIT220992

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Feature Reconstruction of High Resolution SAR Imagery Based on Low Rank Matrix Completion

doi: 10.11999/JEIT220992

Tianjin Key Laboratory for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China

Funds: The National Natural Science Foundation of China (62271487)

Received Date: 2022-07-26
Rev Recd Date: 2022-10-09

Available Online: 2022-10-11

Publish Date: 2023-08-21

Abstract

Abstract

In a countermeasure electromagnetic environment, airborne Synthetic Aperture Radar (SAR) is prone to electronic interference, which makes some echo pulses unavailable, resulting in partial data loss of the SAR echo and limited imaging performance. Thus, a Feature Reconstruction SAR (FR-SAR) imaging algorithm based on low-rank matrix completion is proposed. By considering the low-rank characteristics of the echoed data, the nonzero column number of rows or columns is obtained through matrix decomposition, and the nonzero column number is convexly optimized by Factor Group-Sparse Regularization (FGSR) to obtain the correlation between SAR echoes, to achieve data completion. Additionally, the proposed algorithm in the rank function is more accurate than the conventional nuclear function. Meanwhile, a sparse prior is introduced into the regularization model to improve the noise suppression and super-resolution performance. The Alternating Direction Method of Multipliers (ADMM) is used to realize a collaborative solution between matrix completion and sparse feature enhancement. The FR-SAR algorithm is more efficient because it does not use Singular Value Decomposition (SVD). Simulated and measured data verify the effectiveness of the FR-SAR algorithm. The recovery abilities of the proposed and traditional algorithms are quantitatively compared using a Phase Transition Diagram (PTD), establishing the superiority of the FR-SAR algorithm.
- Synthetic Aperture Radar(SAR),
- Matrix Completion(MC),
- Compressed Sensing(CS),
- Alternating Direction Method of Multipliers(ADMM)

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References(23)

References

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