一种分布式阵列无模糊波达方向估计方法
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摘要
为解决分布式阵列应用常规算法估计波达方向(DOA)时出现的角度模糊问题,提出一种基于压缩感知(CS)理论的无模糊DOA估计方法.利用新方法对分布式阵列的接收信号分别通过直接采样和随机矩阵两种压缩采样方式进行二次采样,将接收信号转换为CS理论所需的随机观测数据,并利用CS重构算法将目标DOA信息从观测数据中高概率、无模糊地获取.将新方法与多重信号分类法(MUSIC)和旋转不变子空间算法(ESPRIT)等经典常规DOA估计算法的运算量进行了详细对比,指出新方法的运算量更小.通过与现有分布式DOA估计方法的仿真实验对比,验证了新方法的有效性,并分析分布式阵列接收阵元数的改变对新方法 DOA估计精度的影响.
In order to solve the ambiguity problem of direction-of-arrival(DOA)with distributed arrays by using convention methods,a new unambiguous DOA estimation method based on Compressive Sensing(CS)was proposed.The echo signal was resampled by the proposed method with direct sampling mode or random matrix mode and converted into the random observed data which can be properly solved by CS theory.The DOA of target was calculated probability and ambiguously by CS reconstruction method.Through the analysis of computational complexity between the proposed method,Multiple Signal Classification(MUSIC),and Estimation of Signal Parameters via Rotational Invariance Techniques(ESPRIT),the conclusion is proved that the proposed method is faster than the others.Simulation results show the good performance of the proposed method.The influence of changing number of sensors on the proposed method was also analyzed.
In order to solve the ambiguity problem of direction-of-arrival(DOA)with distributed arrays by using convention methods,a new unambiguous DOA estimation method based on Compressive Sensing(CS)was proposed.The echo signal was resampled by the proposed method with direct sampling mode or random matrix mode and converted into the random observed data which can be properly solved by CS theory.The DOA of target was calculated probability and ambiguously by CS reconstruction method.Through the analysis of computational complexity between the proposed method,Multiple Signal Classification(MUSIC),and Estimation of Signal Parameters via Rotational Invariance Techniques(ESPRIT),the conclusion is proved that the proposed method is faster than the others.Simulation results show the good performance of the proposed method.The influence of changing number of sensors on the proposed method was also analyzed.
引文
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[2]NORIS J S G.Wirelessly networks for beamforming in distributed phased array radar[D].California:Naval Postgraduate School,2007.
[3]TORNAZAKIS I.Development of a distributed digital array radar[D].California:Naval Postgraduate School,2008.
[4]马严,陈伯孝.双基地分布式阵列MIMO雷达的DOA和DOD估计方法[J].电波科学学报,2016,31(2):228-235.MA Y,CHEN B X.DOD and DOA estimation in bistatic MIMO radar with distributed subarray antennas[J].Chinese Journal of Radio Science,2016,31(2):228-235.
[5]郑桂妹,杨明磊,陈伯孝,等.干涉式矢量传感器MIMO雷达的DOD/DOA和极化联合估计[J].电子信息学报,2012,34(11):2635-2641.ZHENG G M,YANG M L,CHEN B X,et al.Joint DOD/DOA and polarization estimation for interferometric MIMO radar with electromagnetic[J].Journal of Electronics&Information Technology,2012,34(11):2635-2641.
[6]陈根华,谭文群.基于正定优化的分布式阵列舰载米波雷达的高分辨角度估计[J].南昌工程学院学报,2016,35(1):46-51.CHEN G H,TAN W Q.High resolution DOA estimation based on SDP for distributed subarray antenna shipborne VHF radar[J].Journal of Nanchang Institute of Technology,2016,35(1):46-51.
[7]陈根华,陈伯孝,杨明磊.分布式相参阵列及其二维高精度方向估计[J].电子与信息学报,2012,34(11):2621-2627.CHEN G H,CHEN B X,YANG M L.High accuracy 2-D angle estimation using distributed coherent arrays[J].Journal of Electronics&Information Technology,2012,34(11):2621-2627.
[8]CHEN G H,CHEN B X.Eigenstructure-based ambiguity resolution algorithm for distributed subarray antennas VHF radar[J].Electronics Letters,2012,48(13):788-789.
[9]马严,陈伯孝,杨明磊,等.基于ESPRIT的多基线分布式阵列DOA估计方法[J].系统工程与电子技术,2014,36(8):1453-1459.MA Y,CHEN B X,YANG M L,et al.Multi-baseline distributed array DOA estimation using ESPRIT algorithm[J].System Engineering and Electronics,2014,36(8):1453-1459.
[10]HE Z,ZHAO Z,NIE Z,et al.Method of solving ambiguity of sparse array via power estimation based on MUSIC algorithm[J].Signal Processing,2012,92(2):542-546.
[11]DONOHO D.Compressed sensing[J].IEEE Transaction on Information Theory,2006,52(4):1289-1306.
[12]吴敏,邢孟道,张磊.基于压缩感知的二维联合超分辨ISAR成像算法[J].电子与信息学报,2014,36(1):187-193.WU M,XING M D,ZHANG L.Two dimensional joint super-resolution ISAR imaging algorithm based on compressive sensing[J].Journal of Electronics&Information Technology,2014,36(1):187-193.
[13]王伟,张斌,李欣.基于混合匹配追踪算法的MIMO雷达系数成像方法[J].电子与信息学报,2016,38(10):2415-2422.WANG W,ZHANG B,LI X.An imaging method for MIMO radar based on hybrid matching pursuit[J].Journal of Electronics&Information Technology,2016,38(10):2415-2422.
[14]YU Y,PETROPULU A P,POOR H V.Measurement matrix design for compressive sensing-based MIMO radar[J].IEEE Transactions on Signal Processing,2011,59(11):5338-5352.
[15]王赞.分布式双基地波雷达射频干扰与杂波等问题研究[D].西安:西安电子科技大学,2013.WANG Z.Study on some key technologies and issues of high-frequency surface wave radar[D].Xi'an:Xidian University,2013.
[16]傅迎华.可压缩传感重建算法与QR分解[J].计算机应用,2008,28(9):2300-2302.FU Y H.Reconstruction of compressive sensing and semi-QR factorization[J].Computer Applications,2008,28(9):2300-2302.
[17]TROPP J A,GILBERT A C.Signal recovery from random measurements via orthogonal matching pursuit[J].IEEE Transactions on Information Theory,2007,53(12):4655-4666.