Set-membership binormalized data-reusing LMS algorithms

• Published in: IEEE transactions on signal processing Vol. 51; no. 1; pp. 124 - 134
• Main Authors: Diniz, P.S.R., Werner, S.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.01.2003; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive filters; Algorithm design and analysis; Algorithms; Applied sciences; Closed-form solution; Computational complexity; Computational modeling; Computer simulation; Convergence; Data analysis; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Exact solutions; Filtering; Filtering algorithms; Filtration; Information, signal and communications theory; Least squares approximation; Mathematical analysis; Signal analysis; Signal and communications theory; Signal, noise; Telecommunications and information theory; Performance evaluation; Statistical analysis; Adaptive filtering; Signal processing; Data processing; Algorithm; Least mean squares methods
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2002.806562

This paper presents and analyzes novel data selective normalized adaptive filtering algorithms with two data reuses. The algorithms [the set-membership binormalized LMS (SM-BN-DRLMS) algorithms] are derived using the concept of set-membership filtering (SMF). These algorithms can be regarded as generalizations of the previously proposed set-membership NLMS (SM-NLMS) algorithm. They include two constraint sets in order to construct a space of feasible solutions for the coefficient updates. The algorithms include data-dependent step sizes that provide fast convergence and low-excess mean-squared error (MSE). Convergence analyzes in the mean squared sense are presented, and closed-form expressions are given for both white and colored input signals. Simulation results show good performance of the algorithms in terms of convergence speed, final misadjustment, and reduced computational complexity.