Proportionate Recursive Maximum Correntropy Criterion Adaptive Filtering Algorithms and their Performance Analysis

TL;DR

This paper introduces sparsity-aware recursive MCC algorithms with proportionate updating, enhancing outlier robustness and performance in sparse system identification, supported by theoretical analysis and numerical simulations.

Contribution

It proposes two novel sparsity-aware RMCC algorithms incorporating proportionate updating, with stability analysis and optimal parameter selection for nonstationary environments.

Findings

CPRMCC outperforms individual PRMCC filters in steady state.

Proposed algorithms demonstrate improved robustness in sparse system identification.

Theoretical analysis aligns with simulation results.

Abstract

The maximum correntropy criterion (MCC) has been employed to design outlier-robust adaptive filtering algorithms, among which the recursive MCC (RMCC) algorithm is a typical one. Motivated by the success of our recently proposed proportionate recursive least squares (PRLS) algorithm for sparse system identification, we propose to introduce the proportionate updating (PU) mechanism into the RMCC, leading to two sparsity-aware RMCC algorithms: the proportionate recursive MCC (PRMCC) algorithm and the combinational PRMCC (CPRMCC) algorithm. The CPRMCC is implemented as an adaptive convex combination of two PRMCC filters. For PRMCC, its stability condition and mean-square performance were analyzed. Based on the analysis, optimal parameter selection in nonstationary environments was obtained. Performance study of CPRMCC was also provided and showed that the CPRMCC performs at least as well as the better component PRMCC filter in steady state. Numerical simulations of sparse system identification corroborate the advantage of proposed algorithms as well as the validity of theoretical analysis.