Interference-Normalised Least Mean Square Algorithm

TL;DR

The paper introduces the INLMS algorithm, a robust adaptive filtering method that effectively handles highly non-stationary interference signals, outperforming previous gradient-adaptive learning rate algorithms.

Contribution

It extends the gradient-adaptive learning rate approach to non-stationary signals, enabling robust filtering in challenging interference scenarios.

Findings

Effective in highly non-stationary interference environments

Outperforms previous gradient-adaptive algorithms

Demonstrates robustness in dynamic signal conditions

Abstract

An interference-normalised least mean square (INLMS) algorithm for robust adaptive filtering is proposed. The INLMS algorithm extends the gradient-adaptive learning rate approach to the case where the signals are non-stationary. In particular, we show that the INLMS algorithm can work even for highly non-stationary interference signals, where previous gradient-adaptive learning rate algorithms fail.