MUSIC algorithm based on eigenvalue clustering

Abstract

The traditional MUSIC algorithm needs to know the number of target signal sources in advance, and further determine the dimensions of signal subspace and noise subspace, and finally search for spectral peaks. In engineering, it is impossible to predict the number of target signal sources to be measured. To solve the above-mentioned problem, an improved MUSIC algorithm without estimating the number of target signal sources is proposed. In the present algorithm, all eigenvectors of covariance matrix are regarded as noise subspace for spectral estimation, but the existence of signal subspace will make the result unreliable. In order to make the estimation result more accurate, a new weighting method for the spectral estimation results of noise subspace and signal subspace is proposed. The simulation results show that the improved algorithm can accurately estimate the number and direction of signal sources when the number of signal sources is unknown, and has greater practicability than the traditional MUSIC algorithm. In addition, the improved algorithm has better robustness.