  This letter proposes a novel adaptive reduced-rank filtering scheme based on
joint iterative optimization of adaptive filters. The novel scheme consists of
a joint iterative optimization of a bank of full-rank adaptive filters that
forms the projection matrix and an adaptive reduced-rank filter that operates
at 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-rank
filter and low-complexity normalized least-mean squares (NLMS) adaptive
algorithms for its efficient implementation. Simulations for an interference
suppression application show that the proposed scheme outperforms in
convergence and tracking the state-ofthe- art reduced-rank schemes at
significantly lower complexity.
