Recursive EM algorithm with adaptive step size

We study the recursive EM algorithm with adaptive step size (REMA) in this work. Recursive EM is a stochastic approximation procedure for finding the maximum likelihood (ML) estimate. Favorite features of recursive EM include strong consistency, asymptotic normality and simple implementation. Howeve...

Full description

Saved in:
Bibliographic Details
Published in:2003 7th International Symposium on Signal Processing and Its Applications Vol. 2; pp. 519 - 522 vol.2
Main Authors: Pei-Jung Chung, Bohme, J.F.
Format: Conference Proceeding
Language:English
Published: IEEE 2003
Subjects:
Approximation algorithms
Convergence of numerical methods
Covariance matrix
Direction of arrival estimation
Equations
Guidelines
Maximum likelihood estimation
Probability density function
Recursive estimation
Stochastic processes
ISBN:0780379462, 9780780379466
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We study the recursive EM algorithm with adaptive step size (REMA) in this work. Recursive EM is a stochastic approximation procedure for finding the maximum likelihood (ML) estimate. Favorite features of recursive EM include strong consistency, asymptotic normality and simple implementation. However, the convergence rate associated with recursive EM is not optimal. To obtain a good convergence rate without losing the advantage of simple implementation, we propose an adaptive procedure to determine the step size at each recursion. More importantly, this approach provides a guideline for automatic design of the step size. Feasibility of REMA in practical application is demonstrated by its application to the direction finding problem. Numerical results show that the proposed method leads to satisfying convergence behavior in various scenarios.
ISBN:0780379462
9780780379466
DOI:10.1109/ISSPA.2003.1224928