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基于压缩转发的协作MIMO雷达成像算法 被引量:2

Cooperative MIMO Radar Imaging Algorithm Based on Compressing-and-Forward Scheme
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摘要 以实现地面目标的快速、高分辨率成像为目的,本文提出了一种基于压缩感知和协作通信技术的解决方案。在分析压缩感知理论和传统协作MIMO雷达成像算法的基础上,提出了基于匹配滤波器的协作MIMO雷达回波信号的稀疏表示方法和用于恢复重构的基函数,并建立了基于压缩转发的协作MIMO雷达系统模型。该系统主要由收发雷达、转发节点和压缩感知成像处理中心组成,转发节点利用模拟/信息转换(AIC)测量框架将雷达回波数据压缩后转发,压缩感知成像处理中心接收到各转发节点转发的数据后,利用正交匹配追踪算法(OMP)进行距离向压缩和方位向压缩,从而实现快速、高分辨率成像。仿真结果表明,该方法比传统MIMO雷达对各转发节点的传输负荷要求低,成像速度快,目标旁瓣低,成像效果好。 For the purpose of obtaining high-resolution image of ground target with high speed,this paper proposes a technical approach based on the theory of compressive sensing(CS) and cooperative communication.Firstly,on the basis of analyzing the theory of compressive sensing and the imaging algorithm of cooperative MIMO radar,we propose the sparse representation models of the baseband echo under matched filtering and the base function used for signal reconstruction.Then,a cooperative MIMO radar system model based on compressing-and-forward scheme is established.The system mainly consists of a radar with transmitting and receiving antenna, forwarding nodes and a compressed sensing imaging center.Forwarding nodes receive and forward the echoes of cooperative MIMO radar using the analog-to-information conversion(AIC) measure framework.Finally,the orthogonal matching pursuit(OMP) reconstruction algorithm for range compression and azimuth compression is studied,and fast,high-resolution imaging is implemented.Simulation results show that compared to the traditional MIMO radar,the proposed method requires low transmitting load of the forwarding nodes and can imaging with high speed and low sidelobe.
出处 《信号处理》 CSCD 北大核心 2011年第4期612-618,共7页 Journal of Signal Processing
基金 国家自然基金资助课题(编队卫星SAR空时信号处理研究:60971081)
关键词 压缩感知(CS) 协作MIMO雷达 正交匹配追踪算法(OMP) 雷达成像 compressive sensing(CS) cooperative MIMO radar orthogonal matching pursuit(OMP) Radar imaging
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参考文献17

  • 1刘永坦.雷达成像技术[M].哈尔滨工业大学出版社,2001.
  • 2Sendonaris A. , Erkip E. , Aazhang. User cooperation diversity-part I: system description [ J ]. IEEE Transaction Communications, 2003. 51 ( 11 ) : 1927-1938.
  • 3Sendonaris A. , Erkip E. , Aazhang. User cooperation diversity-part II: implementation aspects and performance analysis[ J ]. IEEE Transaction Communications, 2003, 51(11) : 1939-1948.
  • 4Aittomaki T. , Koivunen V.. Performance of MIMO Radar With Angular Diversity Under Swerling Scattering Models. IEEE Journal of Selected Topics in Signal Processing, Feb. 2010, vol. 4, no. 1, pp. 101-114.
  • 5Z. Li, Z. Bao, H. Wang. Performance hnprovement for Constellation SAR using Signal Processing Techniques. IEEE Transactions on Aerospace and Electronic Systems, 2006, vol 42, pp. 436-452.
  • 6Robey F C, Coutts S, and Weikle D,et al.. MIMO radar theory and experimental results. Conference Record of the Thirty-Eighth Asilomar Conference on Signals, System and Computers, California, Nov. 2004:300-304.
  • 7Li J, Stoica P, and Zheng X. Signal synthesis and receiver design for MIMO radar imaging. IEEE Transactions on Siaaaal Processing. 2008. 56 ( 8 ) : 3959-3968.
  • 8Li J and Stoica P. MIMO radar with colocated antennas. IEEE Signal Processing Magazine, 2007,24 ( 5 ) : 106- 114.
  • 9Haimovich A M, Blum R S, and Cimini L J. MIMO radar with widely separated antennas. IEEE Signal Processing Magazine, 2008, 25 ( 1 ) : 116-129.
  • 10Wang H J and Su Y. Narrowband MIMO radar imaging with two orthogonal linear T/R arrays. The 9th international conference on signal processing proceedings, Beijing, Oct. 2008:2513-2516.

二级参考文献117

  • 1张春梅,尹忠科,肖明霞.基于冗余字典的信号超完备表示与稀疏分解[J].科学通报,2006,51(6):628-633. 被引量:71
  • 2R Baraniuk.A lecture on compressive sensing[J].IEEE Signal Processing Magazine,2007,24(4):118-121.
  • 3Guangming Shi,Jie Lin,Xuyang Chen,Fei Qi,Danhua Liu and Li Zhang.UWB echo signal detection with ultra low rate sampling based on compressed sensing[J].IEEE Trans.On Circuits and Systems-Ⅱ:Express Briefs,2008,55(4):379-383.
  • 4Cand,S E J.Ridgelets:theory and applications[I)].Stanford.Stanford University.1998.
  • 5E Candès,D L Donoho.Curvelets[R].USA:Department of Statistics,Stanford University.1999.
  • 6E L Pennec,S Mallat.Image compression with geometrical wavelets[A].Proc.of IEEE International Conference on Image Processing,ICIP'2000[C].Vancouver,BC:IEEE Computer Society,2000.1:661-664.
  • 7Do,Minh N,Vetterli,Martin.Contourlets:A new directional multiresolution image representation[A].Conference Record of the Asilomar Conference on Signals,Systems and Computers[C].Pacific Groove,CA,United States:IEEE Computer Society.2002.1:497-501.
  • 8G Peyré.Best Basis compressed sensing[J].Lecture Notes in Ccmputer Science,2007,4485:80-91.
  • 9V Temlyakov.Nonlinear Methods of Approximation[R].IMI Research Reports,Dept of Mathematics,University of South Carolina.2001.01-09.
  • 10S Mallat,Z Zhang.Matching pursuits with time-frequency dictionaries[J].IEEE Trans Signal Process,1993,41(12):3397-3415.

共引文献765

同被引文献30

  • 1黄琼,屈乐乐,吴秉横,方广有.压缩感知在超宽带雷达成像中的应用[J].电波科学学报,2010,25(1):77-82. 被引量:25
  • 2E Cands, J Romberg and T Tao, Robust uncertainty prin- ciples: Exact signal reconstruction from highly incomplete frequency information [ J ], IEEE Trans. IT,2006,52 ( 2 ) : 489 -509.
  • 3D.L. Donoho, Compressed sensing [ J ] , IEEE Trans. IT ,2006,52 (4) : 1289-1306.
  • 4J. Yin and T. Chen, direction-of-arrival estimation using a sparse representation of array covariance vectors [ J ], IEEE Trans. SP,2011,59(9) :4489-4493.
  • 5C. Qi, X. Wang, L. Wu, underwater acoustic channel esti- mation based on sparse recovery algorithms [ J ] , IET SP, 2011,5(8) :739-747.
  • 6S. Samadi, M. Cetin, M. A. Masnadi-Shirazi, sparse rep- resentation-based synthetic aperture radar imaging [ J ], IET radar sonar navig. ,2011,5 (2) :182-193.
  • 7X. Zhu and R. Bamler, tomographie SAR inversion by L1- norm regularization-the compressive sensing approach [J], IEEE Trans. GRS,2011,48(10) :3839-3846.
  • 8D. L. Donoho and X. Huo, uncertainty principle and ideal atomic decomposition [ J ], IEEE Trans. IF, 2001,47 (6) ,2845-2862.
  • 9S. Mallat and Z. Zhang. Match pursuits with time-frequen- cy dictionaries [J]. IEEE Trans. SP, 1993,41 (12) : 3397-3415.
  • 10A. GilbertI M. Strauss and J. Tropp. etc.. Algorithmiclinear dimension reduction in the ell-1 norm for sparse vectors[ J]. Submitted for publication,2006.

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