A robust subband adaptive filter algorithm for sparse and block-sparse systems identification
Zahra Habibi, Hadi Zayyani, Mohammad Shams Esfand Abadi
Abstract
This paper presents a new subband adaptive filter (SAF) algorithm for system identification scenario under impulsive interference, named generalized continuous mixed p-norm SAF (GCMPN-SAF) algorithm. The proposed algorithm uses a GCMPN cost function to combat the impulsive interference. To further accelerate the convergence rate in the sparse and the block-sparse system identification processes, the proportionate versions of the proposed algorithm, the L0-norm GCMPN-SAF(L0-GCMPN-SAF) and the block-sparse GCMPN-SAF (BS-GCMPN-SAF) algorithms are also developed. Moreover, the convergence analysis of the proposed algorithm is provided. Simulation results show that the proposed algorithms have a better performance than some other state-of-the-art algorithms in the literature with respect to the convergence rate and the tracking capability.