阵列协方差矩阵与FOCUSS算法的DOA估计方法  

Direction of Arrival Estimation Method Based on Array Covariance Matrix and FOCUSS Algorithm

作　　者：李前言 康春玉[2] 

机构地区：[1]海军大连舰艇学院研究生队,大连116018 [2]海军大连舰艇学院信息作战系,大连116018

出　　处：《舰船电子工程》2015年第9期63-67,143,共6页Ship Electronic Engineering

摘　　要：传统的波达方向(DOA)估计方法往往受到Nyquist采样定理与"瑞利限"的限制,对快拍数、阵元数及信噪比等条件的要求较高,并且不能准确估计信号源的幅度信息。基于目标在空域的稀疏性,针对多维观测向量模型,提出一种正则化的FOCUSS稀疏重构算法,可以有效提高低信噪比条件下的估计性能。阵列接收矩阵快拍数大,含噪声信息多,对高分辨的DOA估计影响较大,而通过对阵列的协方差矩阵求高阶次幂的方法可以有效逼近信号子空间,减小噪声子空间的影响。以阵列接收数据的协方差矩阵作为待分解的数据向量构造稀疏模型,能够使重构信号具有较高的分辨率,对快拍数、阵元数及信噪比等条件的要求更低,对旁瓣抑制效果更好,能够较为准确地估计出信源的幅度信息,且不需要对信源数目进行预估计,体现出明显的优势。Traditional direction of arrival estimation method is always limited with Nyquist sampling theorem and Ray‐leigh limit ,requires better condition such as snapshot number ,sensor number and SNR .It also can’t estimate amplifier in‐formation of source accurately yet .Based on spatial sparsity of targets ,aiming at multiple‐dimension vectors model ,regular‐ized FOCUSS sparse reconstruction algorithm can improve performance of DOA estimation in the condition of low SNR effec‐tively .Array received matrix has large snapshot number and lots of noise information and it may affect higher resolution DOA estimation deeply .High power of the covariance matrix can approach signal subspace effectively ,decrease the influence of noise subspace .Taking the covariance matrix of array received data as data vector to be resolved construct sparse model can improve resolution of reconstructed signal ,and requires less snapshot number ,less sensor number and lower SNR .This method can also restrain sidelobe better and estimate amplifier information of source accurately .Moreover ,it doesn’t need to pre‐estimate the number of sources ,reflects obvious advantage .

关 键 词：波达方向估计 正则化 FOCUSS 算法 稀疏重构 协方差矩阵 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]