色噪声背景下相干信源DOA估计的空间差分平滑算法  

Spatial Difference Smoothing Algorithm for DOA Estimation of Coherent Sources in the Presence of Colored Noise Fields

作　　者：齐崇英[1] 王永良[2] 张永顺[1] 陈辉[2] 

机构地区：[1]空军工程大学导弹学院,陕西三原713800 [2]空军雷达学院重点实验室,湖北武汉430010

出　　处：《电子学报》2005年第7期1314-1318,共5页Acta Electronica Sinica

基　　金：国家自然科学基金(No.60272086);全国高等学校优秀青年教师教学科研奖励计划(TRAPOYT)

摘　　要：文中提出了一种色噪声背景下相干信源波达方向(DOA)估计的新算法-空间差分平滑(SDS)算法.SDS算法利用均匀线阵协方差矩阵的Toeplitz分解特性,差分平滑运算,将非相干信源与相关(或相干)信源分开分辨,从而重复利用阵列接收数据,可分辨更多信源.SDS算法可对消空间色噪声,适用于更广泛的未知噪声背景及低信噪比环境.相比常规谱估计算法,SDS算法具有更强的信源过载能力及阵元节省能力,利用少数阵元进行迭代空间平滑运算,还可明显减小SDS算法的计算量.计算机仿真结果证明了SDS算法理论的正确性和有效性.A new algorithm is proposed for direction of arrival （DOA） estimation of coherent sources in the presence of colored noise fields, which is called ＂Spatial Difference Smoothing （SDS）＂ method. By exploiting the property of Teoplitz decomposition of the autocovariance matrix, the SDS method resolves the correlated sources and incoherent sources separately. In this way the output data of the array are used repeatedly, and more sources can be estimated. The SDS method can fully eliminate spatially colored noise, and fit for more general unknown noise fields and low SNR environments. Compared with the conventional methods, the SDS can resolve more sources using the same number of sensors. In addition, the SDS method performs spatial smoothing iteratively utilizing smaller sensor arrays and has smaller computational complexity. Computer simulation results verify the correctness and effectiveness of the proposed SDS method.

关 键 词：波达方向估计 色噪声 相干信源 

分 类 号：TN911.7[电子电信—通信与信息系统]