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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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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Abstract 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.
AbstractList 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.
Author Doroslovački, Miloš I
Wagner, Kevin T
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  organization: Radar Div., Naval Res. Lab., Washington, DC, USA
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  givenname: Miloš I
  surname: Doroslovački
  fullname: Doroslovački, Miloš I
  organization: Dept. of Electr. & Comput. Eng., George Washington Univ., Washington, DC, USA
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Snippet 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...
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StartPage 3738
SubjectTerms 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
Title Proportionate-type NLMS algorithms based on maximization of the joint conditional PDF for the weight deviation vector
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