An Iterative MUSIC Algorithm Research Based On the DOA Estimation

Traditional improved MUSIC algorithm decomposes the signal covariance matrix and then make the signals subspace obtained be orthogonal to the noise subspace, which decreases the effect of the noise. But when the signals interval are very small, traditional improved MUSIC algorithm has been unable to distinguish the signals as the SNR decreases. Based on this problem, an iterative MUSIC is proposed which makes full use of signals subspace and its eigenvalue, on basis of the signals subspace obtained be orthogonal to the noise subspace, constuct spectrum search function and then estimate the signals. Through computer simulation, it has been approved that when the signals interval are very small under the condition of the lower SNR and exist the correlated signals, Iterative MUSIC can estimate the signals accurately that traditional improved MUSIC algorithm can't estimate.