基于Hankel矩阵SVD的均匀线阵信源数估计  

Singular Value Decomposition-Based Source Enumeration of Uniform-Linear Array with Hankel Matrix of Output Correlations

作　　者：张正超 姚桂锦[1] 李月[1] ZHANG Zhengchao;YAO Guijin;LI Yue(College of Communication Engineering, Jilin University, Changchun 130012, China)

机构地区：[1]吉林大学通信工程学院,长春130012

出　　处：《吉林大学学报（信息科学版）》2018年第2期113-120,共8页Journal of Jilin University（Information Science Edition）

基　　金：国家自然科学基金资助项目(41730422);吉林省科技基金资助项目(20130101058JC)

摘　　要：为解决现有信源数目估计方法对不同特性信号的适应性普遍较差的问题,提出了一种均匀线性阵列下基于传感器输出信号相关函数构成的Hankel矩阵的奇异值分解的信源数目估计方法。该方法利用把传感器输出信号相关函数中未知噪声方差项排除的Hankel矩阵的一般形式在信号独立,混合和相干的情况下均能进行信源数目估计,且信源数目估计能力超过传感器数目的一半。仿真实验结果表明,该方法信源数目估计的正确概率(PCEs:Probabilities of Correct Enumeration)的分布特征具有不变性,且相较于基于空间平滑技术的信号源数目估计方法具有更稳定的估计性能和更宽的阈值范围。The exsisting enumeration methods or the subsequent modifications can not adapt well to signals of different properties. A SVD-based（Singular Value Decomposition-based） enumeration method of Hankel matrix of the spatial correlations of sensor outputs is proposed for the ULA（Uniform Linear Array）. Utilizing the general form of Hankel matrix of sensor array with the exclusion of the term of the unknown noise variance from the correlations of sensor outputs,the proposed method can estimate the number of sources whether or not source signals are independent and enumeration capability is up to half of sensor number. The simulation results show that distribution pattern of the PCEs（Probabilities of Correct Enumeration） is invariable and the proposed method has more steady enumeration performance and wider threshold scope by comparison with that based on the spatial smoothing scheme.

关 键 词：阵列 HANKEL矩阵 奇异值分解 信源数目估计 独立/相干 

分 类 号：TN911.7[电子电信—通信与信息系统]