Orthogonal Vector Estimation Algorithm Based on Signal Subspace with General Correlation Matrix
Dengshan Huang, Jun Won Kang, Xingzhao Liu, Jie Zhang, Ping Zhao · 2010
Many popular spectral estimation methods, such as Prony, MUSIC, Linear Prediction etc., may fall into the same mathematic problem of extracting signal embedded within noise. In this paper, an improved spectral estimation with general matrix form is proposed, i.e. Orthogonal Vector Spectral Estimation based on Signal Subspace (OVSESS). Since OVSESS is a kind of orthogonal vector, the improved spectral estimation is achieved based on the capability to distinguish signal frequencies. Therefore, the better stabilization in different conditions of SNR (signal to noise ratio ),can be achieved ,results show that higher robustness and efficiency with signal estimation procedure is obtained by using the improved spectral estimation algorithm with the OVSESS.