Interference-Normalised Least Mean Square Algorithm
This work addresses robust adaptive filtering for non-stationary signals, which is incremental as it builds on gradient-adaptive learning rate approaches.
The authors tackled the problem of robust adaptive filtering for non-stationary signals by proposing the Interference-Normalised Least Mean Square (INLMS) algorithm, which extends gradient-adaptive learning rates to handle highly non-stationary interference where previous methods fail.
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.