Proportionate-type NLMS algorithms based on maximization of the joint conditional PDF for the weight deviation vector

In this paper, we present a proportionate-type normalized least mean square algorithm which operates by choosing adaptive gains at each time step in a manner designed to maximize the joint conditional probability that the next-step coefficient estimates reach their optimal values. We compare and sho...

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Bibliographic Details
Published in:2010 IEEE International Conference on Acoustics, Speech and Signal Processing pp. 3738 - 3741
Main Authors: Wagner, Kevin T, Doroslovački, Miloš I
Format: Conference Proceeding
Language:English
Published: IEEE 01.03.2010
Subjects:
Adaptive algorithm
Adaptive filtering
Adaptive filters
Algorithm design and analysis
Convergence
Filtering algorithms
Least mean square algorithms
Least squares approximation
Noise measurement
Probability density function
proportionate-type normalized least mean square (PtNLMS) algorithm
Radar
sparse impulse response
ISBN:9781424442959, 1424442958
ISSN:1520-6149
Online Access:Get full text
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Summary:In this paper, we present a proportionate-type normalized least mean square algorithm which operates by choosing adaptive gains at each time step in a manner designed to maximize the joint conditional probability that the next-step coefficient estimates reach their optimal values. We compare and show that the performance of the joint maximum conditional probability density function (PDF) one-step algorithm is superior to the proportionate normalized least mean square algorithm when operating on a sparse impulse response. We also show that the new algorithm is superior to a previously introduced algorithm which assumed that the conditional PDF could be represented by the product of the marginal conditional PDFs, i.e., that the weight deviations are mutually conditionally independent.
ISBN:9781424442959
1424442958
ISSN:1520-6149
DOI:10.1109/ICASSP.2010.5495871