An interference-normalized 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 nonstationary. In particular, we show that the INLMS algorithm can work even for highly nonstationary interference signals, where previous gradient-adaptive learning rate algorithms fail.
- Adaptive filtering
- Adaptive filters
- Gradient analysis
- Gradient-adaptive learning rate
- Learning systems
- Least mean squares method
- Normalized least mean square (NLMS) algorithm