Performance Analysis of Recursive Unitary MUSIC Algorithm for Iterative DOA Estimation

摘要

This paper analyzes the performance of iterative estimation of DOAs when using recursive unitary MUSIC algorithm. MUSIC requires the eigenvectors in noise subspace of correlation matrix and therefore we have to execute EVD of correlation matrix or SVD of input data matrix. However, the EVD or SVD generally brings us heavy computational load and as a result it would be difficult to do real-time processing in DOA estimation. In this paper, we focus on the recursive unitary MUSIC to improve the estimation efficiency toward real-time DOA estimation. In this algorithm, BiSVD subspace tracking method is employed to carry out iterative DOA estimation. Through computer simulation, the proposed algorithm with adequate initial values is shown to be better than other types of MUSIC algorithm in estimation accuracy and computation time.