DocumentCode :
1032504
Title :
Simple computational methods of the AP algorithm for maximum likelihood localization of multiple radiating sources
Author :
Oh, Seong Keun ; Un, Chong Kwan
Author_Institution :
Samsung Electronics, Seoul, South Korea
Volume :
40
Issue :
11
fYear :
1992
fDate :
11/1/1992 12:00:00 AM
Firstpage :
2848
Lastpage :
2854
Abstract :
Two computational algorithms that reduce significantly the computational complexity per iteration of the alternating projection (AP) algorithm are presented. One is a recursive projection algorithm that utilizes the projection matrix updating formula, and the other is a maximum eigenvector approximation algorithm that approximates the Hermitian maximization problem in every iteration as the problem of maximizing the modulus of the projection onto the maximum eigenvector subspace. By transforming the computation of Hermitian forms into that of only inner products of vectors, these algorithms significantly reduce the computational complexity without noticeable loss in the estimation performance and convergence behavior. Computer simulation results that validate this approximation are included
Keywords :
array signal processing; computational complexity; eigenvalues and eigenfunctions; iterative methods; maximum likelihood estimation; parameter estimation; Hermitian maximization problem; alternating projection algorithm; array processing; computational algorithms; computational complexity; iteration; maximum eigenvector approximation algorithm; maximum likelihood localization; multiple radiating sources; projection matrix updating formula; recursive projection algorithm; sensor array; Filtering; Iterative algorithms; Kalman filters; Maximum likelihood detection; Maximum likelihood estimation; Nonlinear filters; Sensor arrays; Signal processing algorithms; Systolic arrays; Very large scale integration;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/78.165678
Filename :
165678
Link To Document :
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