一种强杂波背景下SAR目标超分辨成像方法

doi:10.3969/j.issn.1001-2400.2016.05.003

Abstract

Abstract:

This paper proposes a super-resolution imaging method to solve the problem of extracting targets and enhancing resolution under a strong clutter in the SAR image. Based on singal correlation, an adaptive clutter threshold is proposed to extract strong scatterers of targets. Then weak scatterers of targets are extracted by the gradient descent method. Finally, target resolution is enhanced to realize super-resolution imaging by the simplified regularization method. Real-measured data of reflectors and the stationary vehicle validate the effectiveness of the proposed method.

Key words: strong clutter, SAR, targets extraction, resolution enhancement, super-resolution imaging

JING Guobin;SHENG Jialian;CHEN Jianlai;SUN Guangcai;XING Mengdao;BAO Zheng. Super-resolution imaging method for the SAR target in a strong clutter scene[J].Journal of Xidian University, 2016, 43(5): 12-17+87.

References

［1］邢孟道, 保铮, 李真芳, 等. 雷达成像算法进展［M］. 北京: 电子工业出版社, 2014: 11-45.
［2］王洁. 机载雷达杂波抑制技术的研究［D］. 成都：电子科技大学, 2006.
［3］陈家兵, 崔凯峰. 机载雷达地杂波数据的分析与处理［J］. 电子工程师, 2006, 32(7): 11-12.
CHEN Jiabing, CUI Kaifeng. Research and Process on Groud Clutters Data of Airborne Radar ［J］. Electronic Engineer, 2006, 32(7): 11-12.
［4］刘小洋, 李勇, 李瑞克. 机载雷达杂波抑制优化与性能分析［J］. 计算机仿真, 2013, 30(4): 108-111.
LIU Xiaoyang, LI Yong, LI Ruike.Clutter Suppression Optimize and Performance Analysis of Airborne Radar Clutter［J］. Computer Simulation, 2013, 30(4): 108-111.
［5］ BORISON S L, BOWLING S B, CUTOMO K M. Superresolution Methods for Wideband Radar［J］. The Lincoln Laboratory Journal, 1992, 5(3): 441-461.
［6］ GUGLIELMI V, CASTANIE F, PIAU P. Application of Superresolution Methods to Synthetic Aperture Radar Data［C］//Proceedings of International Geoscience and Remote Sensing Symposium: 3. Piscataway: IEEE, 1995: 2289-2291.
［7］ GARBRIEL W F. Improved Range Superresolution via Bangwidth Extrapolation［C］//Proceeding of the National Radar Conference. Piscataway: IEEE, 1993: 123-127.
［8］高博, 王俊, 原慧. 一种参数自适应的SAR 图像去噪方法［J］. 西安电子科技大学学报, 2015, 42(5): 71-76.
GAO Bo, WANG Jun, YUAN Hui. Parameter Adaptive SAR Image Denoising Method［J］. Journal of Xidian University, 2015, 42(5): 71-76.
［9］ NING Q, CHEN K, LI Y, et al. Image Super-resolution via Analysis Sparse Prior ［J］. IEEE Signal Processing Letters, 2013, 20(4): 399-402.
［10］ ZHANG S X, XING M D, XIA X G. Robust Clutter Suppression and Moving Target Imaging Approach for Multichannel in Azimuth High-resolution and Wide-swath Synthetic Aperture Radar［J］. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(2): 687-709.
［11］盛佳恋, 张磊, 邢孟道, 等. 一种利用稀疏统计特性的超分辨ISAR成像方法［J］. 西安电子科技大学学报, 2012, 39(6): 65-71.
SHENG Jialian, ZHANG Lei, XING Mengdao, et al. Super-resloution ISAR Imaging Method with Sprase Statistics［J］. Journal of Xidian University, 2012, 39(6): 65-71.
［12］ TIAN Y, YAP K. Joint Iamge Registration and Super-resolution from Low-resolution Images with Zoonimg Motion ［J］. IEEE Transactions on Circuits and Systems for Video Technology, 2013, 23(7): 1224-1234.
［13］王正明, 朱炬波. SAR图像提高分辨率技术［M］. 北京: 科学出版社, 2006: 131-153.

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Super-resolution imaging method for the SAR target in a strong clutter scene

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