Design of a sparse recursive inverse adaptive algorithm for system identification

Abstract

Based on the developments in the field of compressive sensing in recent years, several LMS-based algorithms have been developed for sparse system identification. These adaptive algorithms combine a l(1)-norm penalty with the the original cost function of the LMS to create a zero attractor (ZA) and hence utilize the sparsity in the filter taps during the adaptation process. In this paper, we propose a new adaptive algorithm to achieve faster convergence rate and lower mean-square deviation under sparsity assumption of impulse response. The proposed modifications employ the recursive inverse adaptive filtering (RI) scheme and the zero attractor to generate the ZA-RI algorithm. Simulation results demonstrate that the proposed modifications result in significant performance gain in comparison to the conventional LMS-based methods.