逆合成孔径成像激光雷达低信噪比稀疏多孔径成像方法研究

臧博; 张磊; 唐禹; 邢孟道

doi:10.3724/SP.J.1146.2010.00669

逆合成孔径成像激光雷达低信噪比稀疏多孔径成像方法研究

doi: 10.3724/SP.J.1146.2010.00669

基金项目:

国家973计划项目(2010CB731903)和国家自然科学基金委员会与中国民用航空局联合项目(61001210)资助课题

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出版历程
- 收稿日期: 2010-06-29
- 修回日期: 2010-09-16
- 刊出日期: 2010-12-19

Study on Sparse Aperture of Inverse Synthetic Aperture Imaging Ladar with Low SNR

摘要

摘要: 受目标非合作特性的影响，逆合成孔径成像激光雷达(ISAIL)回波存在缺失；同时受大气衰减和自然背景光等因素的影响，ISAIL回波信号信噪比较低，因此，常规的稀疏多孔径成像方法不再适用。针对上述问题，该文提出了一种结合压缩感知(CS)和权矩阵的稀疏多孔径成像方法。首先，通过基于CS的稀疏多孔径成像方法对原始数据处理，得到目标像的支撑域；然后，据此建立权矩阵，优化采用CS重构时的代价函数，对稀疏多孔径ISAIL原始数据进行成像处理，利用不完整的回波信号获得高分辨目标像。此算法具有较好的抗噪能力。采用室内ISAIL系统实测数据验证了算法的有效性。
- 逆合成孔径成像激光雷达(ISAIL) /
- 稀疏孔径 /
- 压缩感知(CS) /
- 权矩阵 /
- 低信噪比
Abstract: The traditional sparse aperture methods are not suitable for signal processing of Inverse Synthetic Aperture Imaging Ladar (ISAIL) for the following reasons. One is the lack of echoes caused by the noncooperation characteristic of the target, the other is the low Signal-to-Noise Ratio (SNR) caused by the influence of atmosphere attenuation and natural background light. For this reason, a novel sparse aperture imaging method is proposed in this paper which combines the Compressed Sensing (CS) with the weighted matrix. Through the preprocessing of CS based imaging, the supporting field of the target can be obtained, with which the weighted matrix can be constructed. Then the cost function is optimized using the weighted matrix. Finally, with this new cost function, the high resolution image can be achieved using the incomplete original echoes from the sparse aperture ISAIL. This new method is robust in front of strong noise. The feasibility and effectiveness of the method are validated by the measured data of the indoor ISAIL system.
- Inverse Synthetic Aperture Imaging Ladar (ISAIL) /
- Sparse aperture /
- Compressed Sensing (CS) /
- Weighted matrix /
- Low SNR

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参考文献(1)

Buck J R, Krause B W, and Malm A I R, et al.. Synthetic Aperture Imaging at Optical Wavelengths [C]. CLEO/QELS 2009. Baltimore, MD. 2009: 1-2.[2]Gupta I J，Beals M J, and Moghaddar A. Data extrapolation for high resolution radar imaging[J].IEEE Transaction on Antennas and Propagation.1994, 42(11):1540-1545[3]戴永江. 激光雷达原理[M]. 北京：国防工业出版社，2002: 16-30.[4]Larsson E G and Liu G. High-resolution SAR imaging with angular diversity[J].IEEE Transaction on Aerospace and Electronic Systems.2001, 37(4):1359-1372[5]Cuomo K M, Piou J E, and Mayhan J T. Ultrawide-band coherent processing[J].IEEE Transaction on Antennas and Propagation.1999, 47(6):1094-1107[6]王琦. 空间目标ISAR 成像的研究[D]. [博士论文], 西安电子科技大学，2007, 10.[7]保铮, 邢孟道, 王彤. 雷达成像技术[M]. 北京: 电子工业出版社, 2005: 25-29.[8]Donoho D L. Compressed sensing [J].IEEE Transaction on Information Theory.2006, 52(4):1289-1306[9]Haupt J D. New theory and methods in adaptive and compressive sampling for sparse discovery [D]. [Ph.D. dissertation], University of Wisconsin-Madison, 2009.[10]Cands E, Romberg J, and Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information [J].IEEE Transaction on Information Theory.2006, 52(2):489-509[11]Cands E and Tao T. Near-optimal signal recovery from random projections: universal encoding strategies [J].IEEE Transaction on Information Theory.2006, 52(12):5406-5425[12]李军，邢孟道，张磊，吴顺君. 一种高分辨的稀疏孔径ISAR成像方法[J]. 西安电子科技大学学报, 2010, 37(3): 441-446, 453.Li Jun, Xing Meng-dao, Zhang Lei, and Wu Sun-jun. High resolution imaging method for the sparse aperture of ISAR [J]. Journal of Xidian University, 2010, 37(3):441-446, 453.[13]Zhang Lei, Xing Meng-dao, and Qiu Chen-wei. Achieving higher resolution ISAR imaging with limited pulses via compressed sampling[J].IEEE Geoscience and Remote Sensing Letters.2009, 6(3):567-571[14]郭亮. 合成孔径成像激光雷达实验与算法研究. [博士论文], 西安电子科技大学, 2009, 12.

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