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基于时频点聚类的LFM信号波达方向估计

DOA estimation of LFM signals based on time-frequency points clustering
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摘要 基于空间时频分布(spatial time-frequency distribution,STFD)的多重信号分类(multiple signal classification,MUSIC)算法常用于非平稳信号波达方向(direction of arrival,DOA)估计,其关键是选取合适的信号时频点.文中针对传统时频MUSIC算法不能提取各信号时频点且在小角度间隔时估计性能不佳的问题,以线性调频(line frequency modulation,LFM)信号为研究对象,提出了基于时频点聚类的DOA估计算法.该算法首先对阵列接收信号进行白化,利用白化后的接收信号构造STFD矩阵,达到抑制STFD矩阵的交叉项、突出信号自项的目的,然后利用K均值聚类提取各信号时频点,最后运用MUSIC算法估计DOA.对不同角度间隔和不同信噪比时三种算法的估计均方根误差进行了仿真对比,结果表明:相比经典时频MUSIC算法,文中算法在小角度间隔和低信噪比时有更好的估计性能. The multiple signal classification(MUSIC)algorithm based on spatial time-frequency distribution(STFD)is investigated for direction-of-arrival(DOA)estimation of non-stationary signals,and its key step is to select the appropriate time-frequency points.Aiming at the problems that traditional timefrequency MUSIC(TF-MUSIC)algorithm can not extract the time-frequency points of each source and its poor performance in the case of small angle spacing,this paper proposes a novel DOA estimation algorithm for line frequency modulation(LFM)signals based on time-frequency point clustering.Firstly,the algorithm whitens the array receiving signals,and constructs the STFD matrix using the whitened receiving signals,which can suppress the cross-terms and give prominence to the auto-terms.Then,the algorithm extracts the time-frequency points of each signal by utilizing K-means-clustering.Finally,the MUSIC algorithm is used to estimate the DOA.The root mean square error(RMSE)of three different algorithms in different angle interval and different signal-to-noise ratio(SNR)are simulated respectively.Compared with two classical time-frequency music algorithms,this algorithm has better estimation performance at small angle intervals and low SNR.
作者 周围 袁媛 邵海宁 郭梦雨 ZHOU Wei;YUAN Yuan;SHAO Haining;GUO Mengyu(College of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;Chongqing Key Laboratory of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China)
出处 《电波科学学报》 EI CSCD 北大核心 2018年第1期64-70,共7页 Chinese Journal of Radio Science
基金 重庆市基础与前沿研究计划项目(No.cstc2015jcyj A40040) 重庆邮电大学"文峰骨干教师培养计划项目"
关键词 波达方向 线性调频信号 K均值聚类 空间时频分布矩阵 direction of arrival LFM Signals K-means clustering STFD matrix
作者简介 联系人:周围E-mail:zhouwei1020@263.net,周围(1971-),男,重庆人,教授,硕士生导师,博士,主要研究领域:通信系统及信号处理、无线移动通信技术、阵列信号处理等.;袁媛(1993-),女,四川人,硕士研究生,主要研究方向为无线移动通信、阵列信号处理.;邵海宁(1991-),男,河南人,硕士研究生,主要研究方向为无线移动通信、数字信号处理与传输.
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  • 1程伟,左继章.基于时空结构的阵列信号三维参数同时估计方法[J].通信学报,2004,25(10):67-74. 被引量:5
  • 2叶中付,向利,徐旭.基于信息论准则的信源个数估计算法改进[J].电波科学学报,2007,22(4):593-598. 被引量:15
  • 3A. Belouchrani, M. G. Amin, Blind Source Separation Based on Time-Frequency Signal Representations [J]. IEEE Trans. on Signal Processing, 1998,46( 11 ): 2888-2897.
  • 4A. Belouchrani and M. G. Amin. Time-frequency MUSIC, IEEE Signal Process[J]. Letters, 1999(6) : 109-110.
  • 5Cirillo L A. Narrowband array signal processing using Time- Frequency distributions[D]. Curtin University of Technology, 2007.
  • 6Cohen L. Time,Frequency aualysis[M]. Prentice HM1,1995.
  • 7Cirillo L A,Zoubir A,Amin M G. Blind source separation in the Time-Frequency domain based on multiple hypotheses testing [J]. IEEE Trans. on Signal Processing,2008,56(6): 2267 -2279.
  • 8胡隽.盖氏圆准则信源数估计算法的分析与改进[J].系统工程理论与实践,2007,27(10):124-131. 被引量:3
  • 9CHEN W, WONG K M, REILLY J P. Detection of the number ofsignals: a predicted eigen-threshold approach[J]. IEEE Transactions on Signal Processing, 1991, 39(5): 1088-1098.
  • 10ZHAO L C, KRISHNAIAH P, BAI Z D. Remarks on certain criteria for detection of number of signals[J]. IEEE Transactions on Acoustics, Speech and Signal Processing, 1987, 35(2): 129-132.

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