Reduced-dimension Root-MUSIC Algorithm Based on Spectral Factorization

The Root MUltiple SIgnal Classification (Root-MUSIC) algorithm uses polynomial rooting instead of spectral search to reduce the computational complexity of Direction-Of-Arrival (DOA) estimation. However, when large numbers of sensors are exploited, this algorithm is still time-consuming. To further reduce the complexity, a novel Reduced-Dimension Root-MUSIC (RD-Root-MUSIC) algorithm based on spectral factorization is proposed, in which the dimension of polynomial involved in the rooting step is efficiently reduced to half. A companion matrix whose eigenvalues correspond to the roots of the reduced-dimension polynomial is further constructed, and the Arnoldi iteration is finally used to calculate only the L largest eigenvalues containing DOA information, where L is the number of signals. Simulation results show that RD-Root-MUSIC has a similar performance with much lower complexity as compared to Root-MUSIC.