波达方向估计的一种改进SVD算法

本文主研究智能天线算法中的关键技术波达方向估计（DOA）。针对相干信号源的信号子空间与噪声子空间相互渗透,导致空间协方差矩阵缺秩从而经典算法失效的问题,本文基于奇异值分解（SVD）算法,提出了一种改进的SVD算法。该算法利用入射信号矩阵的最大特征向量元素包含所有入射信号信息的性质,进行矩阵重构,并对重构矩阵进行特征值分解得到噪声子空间和信号子空间,最后利用经典谱估计算法得到相干信源的入射方向。仿真试验结果表明改进SVD算法性能优于原始算法。

An improved SVD characteristic spectral estimation algorithm in Direction of Arrival Estimation

The paper mainly studied the key technologies of smart antenna algorithm called direction of arrival estimation（DOA）. The rank＇s lack of spatial eovariance matrix caused by the mutual penetration of the coherent source signal subspace and noise subspace lead to the failure of the classic algorithm. The paper provided an improved singular value decomposition （SVD） algorithm based on the classical SVD algorithm. As the maximum eigenvector of the incident signal matrix contains full information of the the incident signal,the reconstructed matrix of incident signal can be decomposed into noise subspace and signal subspace in order to obtain the incident direction of coherent sources using classic spectral estimation algorithm. Experimental simulation shows the performance of the new algorithm is better than the original algorithm.