Two Variable Step-size Adaptive Algorithms for Non-Gaussian Interference Environment using Fractionally Lower-order Moment Minimization

Abstract

Two variable step-size adaptive algorithms using fractionally lower-order moment minimization are proposed for system identification in non-Gaussian interference environment. The two algorithms automatically adjust their step sizes and adapt the weight vector by minimizing the p-th moment of the a posteriori error, where p is the order with 1≤p≤2, thus they are named as variable step-size normalized least mean p-th norm (VSS-NLMP) algorithms. The proposed adaptive VSS-NLMP algorithms are applied to both real- and complex-valued systems using low-complexity time-averaging estimation of the lower-order moments. Simulation results show that the misalignment of the proposed VSS-NLMP algorithms with a smaller p converges faster and achieves lower steady-state error in impulsive interference and/or colored input environment. The adaptive VSS-NLMP algorithms also perform better than the adaptive fixed step-size (FSS) NLMP in both Gaussian and finite-variance impulsive interference environments. A theoretical model for the steady-state excess mean-square error is also provided for both Gaussian and Bernoulli-Gaussian interference. © 2013 Elsevier Inc.

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Bernoulli-Gaussian distribution; Compound K distribution; Fractionally lower-order moment (FLOM) algorithm; Least mean p-moment (LMP) algorithm; Non-Gaussian interference suppression; Robust adaptive filter; Variable step size

International Standard Serial Number (ISSN)

1051-2004

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Elsevier, All rights reserved.

Publication Date

01 Jan 2013

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