Krylov-proportionate adaptive filtering techniques not limited to sparse systems
IEEE Transactions on Signal Processing
Robust reduced-rank adaptive algorithm based on parallel subgradient projection and Krylov subspace
IEEE Transactions on Signal Processing
A unified view of adaptive variable-metric projection algorithms
EURASIP Journal on Advances in Signal Processing
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This paper indicates that an appropriate design of metric leads to significant improvements in the adaptive projected subgradient method (APSM), which unifies a wide range of projection-based algorithms [including normalized least mean square (NLMS) and affine projection algorithm (APA)]. The key is to incorporate a priori (or a posteriori) information on characteristics of an estimandum, a system to be estimated, into the metric design. We propose a family of efficient adaptive filtering algorithms based on a parallel use of quadratic-metric projection, which assigns every point to the nearest point in a closed convex set in a quadratic-metric sense. We present two versions: (1) constant-metric and (2) variable-metric, i.e., the metric function employed is (1) constant and (2) variable among iterations. As a constant-metric version, adaptive parallel quadratic-metric projection (APQP) and adaptive parallel min-max quadratic-metric projection (APMQP) algorithms are naturally derived by APSM, being endowed with desirable properties such as convergence to a point optimal in asymptotic sense. As a variable-metric version, adaptive parallel variable-metric projection (APVP) algorithm is derived by a generalized APSM, enjoying an extended monotone property at each iteration. By employing a simple quadratic-metric, the computational complexity of the proposed algorithms is kept linear with respect to the filter length. Numerical examples demonstrate the remarkable advantages of the proposed algorithms in an application to acoustic echo cancellation.