Proportionate Maximum Versoria Criterion-Based Adaptive Algorithm for Sparse System Identification

Abstract

Proportionate Maximum Versoria Criterion (P-MVC) based adaptive algorithms for unknown sparse system identification problem are proposed in this brief. The conventional proportionate type algorithms used for sparse system identification can work well only under Gaussian assumption due to the dependency on the least mean square error. However, in many real cases, the algorithms have to be also robust in impulsive noise environments. The Maximum Versoria Criteria based adaptive algorithms were found to have good robustness against impulsive noise while the proportionate term in the adaptive algorithm exploits the sparse nature to improve the convergence speed. Hence, to simultaneously have robustness under impulsive environment and improved convergence speed, the P-MVC algorithm and an improved tracking P-MVC version are proposed. The performance analysis indicates that the Excess Mean Square Error (EMSE) is the same as that of MVC adaptive algorithm. Furthermore, simulations in the context of sparse system identification scenario reveal that the proposed algorithms have both robustness and improved performance in impulsive noise environment.