NDLI: Performance analysis of adaptive step-size least mean modulus-Newton algorithm for identification of non-stationary systems

Content Provider	IEEE Xplore Digital Library
Author	Koike, S.
Copyright Year	2012
Description	Author affiliation: Tokyo, Japan (Koike, S.)
Abstract	This paper first reviews least mean modulus-Newton (LMM-Newton) algorithm that combines LMM algorithm for complex-domain adaptive filters with simple recurrent calculation of the inverse covariance matrix of the filter reference input process. The LMM-Newton algorithm is effective in improving the convergence of an adaptive filter with a strongly correlated input, while preserving the robustness of the LMM algorithm against impulsive observation noise. For identification of random walk modeled non-stationary systems, it is known that there exists a step-size value that gives the minimum steady-state error. The paper proposes a new adaptive step-size control algorithm to be combined with the LMM-Newton algorithm that yields adaptive step-size least mean modulus-Newton (ASS-LMM-Newton) algorithm to realize the optimum tracking performance. Through performance analysis and experiment with simulations and theoretical calculations of filter convergence, we demonstrate effectiveness of the proposed ASS-LMM-Newton algorithm in identification of non-stationary systems in the presence of impulse noise.
Starting Page	184
Ending Page	187
File Size	1033017
Page Count	4
File Format	PDF
ISBN	9781467309608
e-ISBN	9781467309615
DOI	10.1109/CSPA.2012.6194715
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-03-23
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Adaptation models Noise Adaptive filters adaptive step size impulsive observation noise Convergence correlated filter input random walk adaptive filter Signal processing algorithms Filtering algorithms Newton's method non-stationary system identification
Content Type	Text
Resource Type	Article

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