To solve the problem of coherent sources using sparse reconstruction method, this paper proposes an improved method for solving coherent sources using the eigenvectors corresponding to the largest eigenvalues after Singular Value Decomposition (SVD) decomposition of received data. The method reconstructs the angle by iterating the feature vector, and reconstructs the angle information accurately without knowing the number of the signal source. Compared with the classical SVD algorithm, the operation speed is faster, and the sparse reconstruction effect is better. Theoretical analysis and simulation results verify the good performance of the algorithm.
季正燕,陈辉,张佳佳,李帅,陆晓飞. 一种基于奇异值分解的解相干算法[J]. 电子与信息学报, 2017, 39(8): 1913-1918.
JI Zhengyan, CHEN Hui, ZHANG Jiajia, LI Shuai, LU Xiaofei. Decorrelation Algorithm Based on Singular Value Decomposition. JEIT, 2017, 39(8): 1913-1918.
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