Selective-Tap Adaptive Filtering With Performance Analysis for Identification of Time-Varying Systems

Abstract

Abstract-Selective-tap algorithms employing the MMax tap selection criterion were originally proposed for low-complexity adaptive filtering. The concept has recently been extended to multichannel adaptive filtering and applied to stereophonic acoustic echo cancellation. This paper first briefly reviews least mean square versions of MMax selective-tap adaptive filtering and then introduces new recursive least squares and affine projection MMax algorithms. We subsequently formulate an analysis of the MMax algorithms for time-varying system identification by modeling the unknown system using a modified Markov process. Analytical results are derived for the tracking performance of MMax selective tap algorithms for normalized least mean square, recursive least squares, and affine projection algorithms. Simulation results are shown to verify the analysis.

Computationally Efficient Self-Tuning Controller for DC–DC Switch Mode Power Converters Based on Partial Update Kalman Filter

“…NLMS algorithm defined by a factor of B = M/N, which results in significant improvement in performance compared to the fully updated NLMS if 0.5N ≤ M < N is selected [12,16].…”

A lowcomplexity fast converging partial update adaptive algorithm employing variable step-size for acoustic echo cancellation

“…It can be seen that the rate of convergence reduces with reducing M as expected. The dependency of the asymptotic performance and rate of convergence on M for MMax-NLMS has been analyzed in [9].…”

“…Block-based and transform domain algorithms which generalized MMax-NLMS [8] have also been proposed. More recently, the MMax tap-selection criterion has been extended to a class of selective-tap algorithms including the MMax affine projection (MMax-AP) and MMax recursive least squares (MMax-RLS) algorithms [9]. The performance of these MMax-based adaptive algorithms for time-varying LRM systems has also been analyzed [9] and extended for the multichannel case [10].…”

“…More recently, the MMax tap-selection criterion has been extended to a class of selective-tap algorithms including the MMax affine projection (MMax-AP) and MMax recursive least squares (MMax-RLS) algorithms [9]. The performance of these MMax-based adaptive algorithms for time-varying LRM systems has also been analyzed [9] and extended for the multichannel case [10]. It has been shown that the performance of MMax tap-selection is better than Periodic-and Sequential-LMS algorithms [11].…”

“…The aim of this paper is to propose a low complexity, fast converging adaptive algorithm for AEC. It has been shown in [9] that the convergence performance of MMax-NLMS is dependent on the step-size when identifying a LRM system. This motivates us to jointly utilize the low complexity of MMax tap-selection and the improvement in convergence performance brought about by a variable step-size.…”