This paper studies the stochastic behavior of the LMS algorithm for a system identification framework when the input signal is a non-stationary white Gaussian process. The unknown system is modeled by the standard random walk model. An approximate theory is developed which is based upon the instantaneous average power in the adaptive filter taps. The stability of the algorithm is investigated using this model. Monte Carlo simulations of the algorithm provides strong support for the theoretical approximation.
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Univ Calif Irvine, Dept Elect Engn & Comp Sci, 1621 Santiago Dr, Newport Beach, CA 92660 USAUniv Calif Irvine, Dept Elect Engn & Comp Sci, 1621 Santiago Dr, Newport Beach, CA 92660 USA
Bershad, Neil J.
Eweda, Eweda
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Future Univ, Dept Elect Engn, New Cairo, EgyptUniv Calif Irvine, Dept Elect Engn & Comp Sci, 1621 Santiago Dr, Newport Beach, CA 92660 USA
Eweda, Eweda
Bermudez, Jose C. M.
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Univ Fed Santa Catarina, Dept Elect Engn, BR-88040900 Florianopolis, SC, BrazilUniv Calif Irvine, Dept Elect Engn & Comp Sci, 1621 Santiago Dr, Newport Beach, CA 92660 USA