期刊文献+

利用压缩感知的逆合成孔径激光雷达成像新方法 被引量:5

Novel imaging method using compressed sensing for the inverse synthetic aperture imaging ladar
下载PDF
导出
摘要 受限于激光调制技术,在较长的积累时间内,逆合成孔径成像激光雷达发射脉冲之间的相干性难以保持,导致方位向散焦,影响成像效果.为此提出了一种在较短相干积累时间内,利用较少回波数据实现同等分辨率的瞬时成像算法.此算法利用压缩感知理论,将逆合成孔径成像激光雷达成像问题转换为利用正交基压缩感知重构稀疏信号的问题,通过优化求解的方式对目标像优化重建,且在低信噪比时通过构造权矩阵,提高算法对目标像的恢复性能.室内逆合成孔径成像激光雷达验证系统实测数据验证了算法的可行性和有效性. Limited by the laser modulation technique, the coherence between transmitting pulses of the inverse synthetic aperture imaging ladar (ISAIL) can not be kept, which leads to the unfocused image in the azimuth direction. Here, an instantaneous imaging algorithm is proposed to achieve the high resolution with less echo data in a short coherence time. Based on the theory of compressed sensing, the ISAIL imaging is converted into a reconstruction issue using the orthogonal base. The reconstruction of the target is resolved by optimizing the function. Under the situation of a low SNR, the performance of the imaging algorithm is improved by constructing a weighted matrix. Experimental results using measured data of the indoor ISAIL system validate the feasibility and effectiveness of our method.
出处 《西安电子科技大学学报》 EI CAS CSCD 北大核心 2010年第6期1027-1032,共6页 Journal of Xidian University
基金 国家部委"973"资助项目(2010CB731903) 中央高校基本科研业务费专项资金资助项目(K50510050008)
关键词 逆合成孔径成像激光雷达 瞬时成像 压缩感知 稀疏信号重构 inverse synthetic aperture imaging ladar instantaneous imaging compressed sensing sparse signal reconstruction
  • 相关文献

参考文献14

  • 1Buck J R,Krause B W,Malm A I R,et al.Synthetic Aperture Imaging at Optical Wavelengths[C]//2009 Conference on Quantum Electronis and Laser Science Conference.Baltimore:IEEE,2009:1-2.
  • 2Donoho D L.Compressed Sensing[J].IEEE Trans on Info Theory,2006,52(4):1289-1306.
  • 3Zhu Chenwei.Stable Recovery of Sparse Signals Via Regularized Minimization[J].IEEE Trans on Info Theory,2008,54(7):3364-3367.
  • 4Varshney K R,Cetin M,Fisher J W,et al.Sparse Representation in Structured Dictionaries With Application to Synthetic Aperture Radar[J].IEEE Trans on Signal Processing,2008,56(8):3548-3561.
  • 5Thayaparan T,Stankovic L,Wernik C,et al.Real-Time Motion Compensation,Image Formation and Image Enhancement of Moving Targets in ISAR and SAR Using S-method Based Approach[J].IET Signal Processing,2008,2(3):247-264.
  • 6Wang Y,Ling H,Chen V C.ISAR Motion Compensation Via Adaptive Joint Time-frequency Techniques[J].IEEE Trans on Aerosp Electron Syst,1998,34(2):670-677.
  • 7Herman M A,Strohmer T.High-Resolution Radar via Compressed Sensing[J].IEEE Trans on Signal Processing,2009,57(6):2275-2284.
  • 8Candès E,Romberg J,Tao T.Robust Uncertainty Principles:Exact Signal Reconstruction from Highly Incomplete Frequency Information[J].IEEE Trans on Inform Theory,2006,52(2):489-509.
  • 9Candès E,Tao T.Near-optimal Signal Recovery from Random Projections:Universal Encoding Strategies[J].IEEE Trans on Information Theory,2006,52(12):5406-5425.
  • 10Pati Y C,Rezaiifar R,Krishnaprasad P S.Orthogonal Matching Pursuit:Recursive Function Approximation with Applications to Wavelet Decomposition[C]//Proceedings of the 27th Annual Asilomar Conference in Signals,Systems,and Computers.Los Alamitos:IEEE,1993:40-44.

二级参考文献12

  • 1Cuomo K M, Piou J E, Mayhan J T. Ultrawide-band Coherent Processing [J]. IEEE Trans on Antennas Propag, 1999, 47(6) : 1094-1107.
  • 2Potter L C, Arun K S. Energy Concentration in Band-limited Extrapolation [J]. IEEE Trans on Acoustics Speech and Signal Processing, 1989, 37(7): 1027-1041.
  • 3Li H J, Farhat N, Shen Y. A New Iterative Algorithm for Extrapolation of Data Available in Multiple Restricted Regions with Application to Radar Imaging [J]. IEEE Trans on AP, 1987, 35(5) : 581 -588.
  • 4Donoho D L. Compressed Sensing [J]. IEEE Trans on Info Theory, 2006, 52(4) : 1289-1306.
  • 5Zhu Chenwei. Stable Recovery of Sparse Signals Via Regularized Minimization [J]. IEEE Trans on Info Theory, 2008, 54(7) : 3364-3367.
  • 6Zhang Lei, Xing Mengdao, Qiu Chenwei. Achieving Higher Resolution ISAR Imaging with Limited Pulses Via Compressed Sampling [J]. IEEE GRS Letter, 2009, 6(3) : 567 -571.
  • 7Rauhut H, Schnass K, Vandergheynst P. Compressed Sensing and Redundant Dictionaries [J]. IEEE Trans on Info Theory, 2008, 54(5): 2210-2219.
  • 8Candes E. The Restricted Isometry Property and Its Implications for Compressed Sensing [J]. Comptes Rendus Mathematique, 2006, 246(9): 589-592.
  • 9Candes E, Romberg J, Tao T. Stable Signal Recovery from Incomplete and Inaccurate Measurements [J]. Communications on Pure and Applied Mathematics, 2005, 59(8) : 1207-1223.
  • 10Herman M, Strohmer T. High-Resolution Radar via Compressed Sensing [J]. IEEE Trans on Signal Processing, 2009, 57(6) : 2275-2284.

共引文献26

同被引文献77

  • 1赖旭东,万幼川.一种针对激光雷达强度图像的滤波算法研究[J].武汉大学学报(信息科学版),2005,30(2):158-160. 被引量:17
  • 2李琦,王永珍,王骐,李自勤.相干激光雷达距离像的噪声抑制算法研究[J].光学学报,2005,25(5):581-584. 被引量:30
  • 3孙剑峰,李琦,陆威,王骐.基于数字信号处理器的激光成像雷达目标识别算法实现[J].中国激光,2006,33(11):1467-1471. 被引量:15
  • 4Donoho D. Compressed Sensing [J]. IEEE Trans on Information Theory, 2006, 52(4) : 1289-1306.
  • 5Candes E, Romberg J, Tao T. Near Optimal Signal Recovery from Random Projections : Universal Encoding Strategies? [J]. IEEE Trans on Information Theory, 2006, 52(12) : 5406-5425.
  • 6Candes E, Wakin M. An Introduction to Compressive Sampling [J]. IEEE Signal Processing Magazine, 2008, 25 (2): 21-30.
  • 7Shannon C E. A Mathematical Theory of Communication [J]. Bell System Tech Journal, 1948, 27(3) : 379-423.
  • 8Ward R. Compressed Sensing with Cross Validation [J]. IEEE Trans on Information Theory, 2009, 55 (12): 5773- 5782.
  • 9Malioutov D M, Sanghavi S R, Willsky A S. Sequential Compressed Sensing [J]. IEEE J Sel Top Sig Proc, 2010, 4(2) 435-444.
  • 10Blelloch G E. Introduction to Data Compression[M]. Pittsburgh: Carnegie Mellon University, 2010.

引证文献5

二级引证文献40

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部