Research on the Method of Sparse Channel Estimation Based on Support Agnostic Bayesian Orthogonal Matching Pursuit
DU Xiu-li, Zhu Kang-Te, Bo Chen, Guo Yuan-Yuan · 2018
According to sparse channel estimation and Bayesian theory, Support Agnostic Bayesian Matching Pursuit (SABMP) is characteristic of inferior convergence. To improve the convergence rate of SABMP effectively, a new method named sparse channel estimation based on Support Agnostic Bayesian Orthogonal Matching Pursuit (SABOMP) is proposed in this paper. In this new method, the orthogonal-method is introduced to increase convergence speed, which makes residual and the column vectors corresponding to the selected model to be orthogonal in each residual update. The simulation results show that the proposed method has high estimation precision, especially under low signal to noise ratio (SNR). Furthermore, compared with the original method, the convergence speed of this method is faster, and the efficiency of sparse channel estimation is higher.