摘要
在均匀线性阵列模型下,特征矢量奇异值分解算法能够对相干信号进行DOA估计,但相干和不相关信号同时存在时,算法的估计会出现错误。针对这一问题,提出了一种修正算法(MESVD),该算法选取经过加权处理的特征向量来构造矩阵,并利用该矩阵进行子空间估计。理论分析和数值仿真证明:修正后的算法能够正确估计相干、相关和不相关信号,估计性能与空间平滑算法(FBSS)相当。
The Extended Singular Value Decomposition（ESVD） algorithm can deal with the coherent signals exactly in Uniform Linear Army（ULA）, but when the coherent signals and the uncorrelated signals inject at the same time, the ESVD algorithm usually gives incorrect result. According to this problem, a modified algorithm （MESVD） is proposed, which uses a weighted eigenvector to construct a matrix for subspaces estimation. Analysis and simulations show that the MESVD algorithm can give right estimation without considering the relativity of signals and its estimation performance is corresponding to that of FBSS algorithm.
出处
《电讯技术》
北大核心
2013年第2期162-165,共4页
Telecommunication Engineering
关键词
DOA估计
相干信号
奇异值分解
不相关信号
DOA estimation
coherent signal
singular value decomposition
uncorrelated signal
