Reduced-Rank Adaptive Filtering Based on Joint Iterative Optimization of Adaptive Filters

摘要

This letter proposes a novel adaptive reduced-rank filtering scheme based onjoint iterative optimization of adaptive filters. The novel scheme consists ofa joint iterative optimization of a bank of full-rank adaptive filters thatforms the projection matrix and an adaptive reduced-rank filter that operatesat the output of the bank of filters. We describe minimum mean-squared error(MMSE) expressions for the design of the projection matrix and the reduced-rankfilter and low-complexity normalized least-mean squares (NLMS) adaptivealgorithms for its efficient implementation. Simulations for an interferencesuppression application show that the proposed scheme outperforms inconvergence and tracking the state-ofthe- art reduced-rank schemes atsignificantly lower complexity.