压缩感知合成孔径雷达射频干扰抑制处理

麦超云; 孙进平; 崔如心; 张冰尘

压缩感知合成孔径雷达射频干扰抑制处理

1.
北京航空航天大学电子信息工程学院, 北京 100191
2. 中国科学院微波成像技术国家重点实验室, 北京 100190

基金项目: 国家973计划资助项目（2010CB731903）

详细信息

中图分类号: TN95
计量
- 文章访问数: 1678
- HTML全文浏览量: 318
- PDF下载量: 410
- 被引次数: 0
出版历程
- 收稿日期: 2013-03-06
- 网络出版日期: 2014-01-20

RFI suppression processing for compressive sensing based SAR imaging

1.
School of Electronic and Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
2. National Key Lab of MW Imaging Technology, Chinese Academy of Sciences, Beijing 100190, China

摘要

摘要: 对基于压缩感知技术的合成孔径雷达（SAR，Synthetic Aperture Radar）成像，射频干扰（RFI，Radio Frequency Interference）的存在会破坏场景稀疏的先验条件，造成成像质量恶化，使得后续的成像处理无法正确完成的问题，提出了一种压缩感知SAR的RFI抑制方法.首先基于RFI在频域的稀疏特征，采用贪婪算法结合最小描述长度（MDL，Minimum Description Length）估计出RFI分量稀疏度；然后对每个脉冲的回波信号，估计RFI信号分量并在时域直接滤除，再应用常规的压缩感知SAR重构算法实现成像处理.L波段SAR数据的仿真处理结果验证了文中方法的有效性.
- 合成孔径雷达 /
- 射频干扰抑制 /
- 压缩感知 /
- 贪婪算法 /
- 最小描述长度
Abstract: To the synthetic aperture radar (SAR) imaging system which uses compressive sensing (CS) technology, radio frequency interference (RFI) would undermine the priori sparse condition and cause deterioration of image quality, making the subsequent reconstruction algorithm complete the imaging process incorrectly. A RFI suppression method of CS SAR was proposed. The greedy algorithm combined with minimum description length (MDL) criteria was used to estimate the RFI components sparsity, according to the RFI sparse characteristics in the frequency domain. For each pulse data, the RFI signal components were estimated and filtered in the time domain directly. Then conventional CS SAR reconstruction algorithm can be applied to achieve imaging output. The simulation results of L-band SAR data verify the effectiveness of this method.
- synthetic aperture radar(SAR) /
- radio frequency interference(RFI)suppression /
- compressive sensing(CS) /
- greedy algorithm /
- minimum description length(MDL)

HTML全文

参考文献(12)

[1]	Miller T, Potter L, McCorkle J.RFI suppression for ultra wideband radar[J]. IEEE Trans Aerosp Electron Syst, 1997, 33(4):1142 -1156
[2]	王彦平, 彭海良, 吴一戎, 等.合成孔径雷达窄带干扰抑制技术综述[J].现代防御技术, 2003, 31(1):46-54 Wang Yanping, Peng Hailiang, Wu Yirong, et al.Summary of narrow band interference suppression in synthetic aperture radar[J].Modern Defence Technology, 2003, 31(1):46-54(in Chinese)
[3]	黄晓涛, 梁甸农.UWB-SAR抑制RFI技术的参数化方法[J].系统工程与电子技术, 2000, 22(2):94-97 Huang Xiaotao, Liang Diannong. Performance evaluation and test of RFI suppression algorithms for UWB-SAR[J].Systems Engineering and Electronics, 2000, 22(2):94-97(in Chinese)
[4]	Candès E, Wakin M.An introduction to compressive sampling[J].IEEE Signal Process Mag, 2008, 25(2):21-30
[5]	Ender J H G. On compressive sensing applied to radar[J].Signal Processing, 2010, 90(5):1402-1414
[6]	Baraniuk R, Steeghs P. Compressive radar imaging[C]//IEEE Radar Conference.Boston, MA:IEEE, 2007:128-133
[7]	Herman M, Strohmer T. High-resolution radar via compressed sensing[J].IEEE Trans Signal Processing, 2009, 57(6): 2275-2284
[8]	刘记红, 徐少坤, 高勋章, 等. 压缩感知雷达成像技术综述[J].信号处理, 2011, 27(2):251-260 Liu Jihong, Xu Shaokun, Gao Xunzhang, et al.A review of radar imaging technique based on compressed sensing[J].Signal Processing, 2011, 27(2):251-260(in Chinese)
[9]	Tropp J A.Greed is good:algorithmic results for sparse approximation[J].IEEE Trans on Info Theory, 2004, 50(10): 2231- 2242
[10]	Pati Y C, Rezaiifar R P, Krishnaprasad S.Orthogonal matching pursuits:recursive function approximation with applications to wavelet decomposition[C]//Proceedings of the 27th Asilomar Conference in Signals, Systems and Computers.Pacific Grove, CA:IEEE, 1993:40-44
[11]	Tropp J A, Gilbert A C.Signal recovery from partial information by orthogonal matching pursuit[J].IEEE Transactions on Information Theory, 2007, 53(12):4655-4666
[12]	Wax M, Kailath T.Detection of signals by information theoretic criteria[J].IEEE Transactions on Speech and Signal Processing, 1985, 33(2):387-392