A Double Proportionate Sparse Adaptive Filter for Impulsive Noise Environments

Abstract

Sparse adaptive filters and impulsive noise robust algorithms have largely been developed along separate tracks, leaving a gap when both properties are needed simultaneously. This letter proposes the double proportionate sparse adaptive filter (DP-SAF), which closes this gap within a single $\mathcal{O}(M)$ update. Two independent diagonal gain matrices are introduced; one scales the adaptation step proportionately to coefficient magnitudes, and the other applies a magnitude-dependent zero-attraction that is strongest for inactive taps. A sign-error update provides robustness against impulsive corruptions. Both gain matrices are derived from a minimum-norm optimization framework. Simulations under a Bernoulli impulsive noise model show that DP-SAF consistently achieves a better steady-state MSD than the competing algorithms while matching or exceeding their convergence speeds.