Mean-Square Performance Analysis of Noise-Robust Normalized Subband Adaptive Filter Algorithm

TL;DR

This paper provides a comprehensive statistical analysis of the noise-robust normalized subband adaptive filter algorithm, including transient and steady-state behavior, without assuming Gaussian inputs or paraunitary filter banks.

Contribution

It introduces a novel analysis method that removes previous assumptions, offering more accurate steady-state expressions and broad applicability.

Findings

The analysis accurately predicts transient and steady-state behavior.

The proposed method outperforms previous analyses in accuracy.

Simulation results validate the theoretical predictions.

Abstract

This paper studies the statistical models of the noise-robust normalized subband adaptive filter (NR-NSAF) algorithm in the mean and mean square deviation senses involving transient-state and steady-state behavior by resorting to the method of the vectorization operation and the Kronecker product. The analysis method does not require the Gaussian input signal. Moreover, the proposed analysis removes the paraunitary assumption imposed on the analysis filter banks as in the existing analyses of subband adaptive algorithms. Simulation results in various conditions demonstrate the effectiveness of our theoretical analysis. For a special form of the algorithm, the proposed steady-state expression is also better accurate than the previous analysis.