Paper Info
Title
Maximum likelihood array processing for stochastic coherent sources
Abstract
Maximum likelihood (ML) estimation in array signal processing for the stochastic noncoherent signal case is well documented in the literature. We focus on the equally relevant case of stochastic coherent signals. Explicit large-sample realizations are derived for the ML estimates of the noise power and the (singular) signal covariance matrix. The asymptotic properties of the estimates are examined, and some numerical examples are provided. In addition, we show the surprising fact that the ML estimates of the signal parameters obtained by ignoring the information that the sources are coherent coincide in large samples with the ML estimates obtained by exploiting the coherent source information. Thus, the ML signal parameter estimator derived for the noncoherent case (or its large-sample realizations) asymptotically achieves the lowest possible estimation error variance (corresponding to the coherent Cramer-Rao bound)
Year
DOI
Venue
1996
10.1109/78.482015
IEEE Transactions on Signal Processing
Keywords
Field
DocType
noncoherent case,coherent cramer-rao,stochastic coherent source,relevant case,stochastic noncoherent signal case,array signal processing,stochastic coherent signal,signal parameter,maximum likelihood array processing,ml estimate,coherent source information,ml signal parameter estimator,stochastic processes,cramer rao bound,noise,parameter estimation,maximum likelihood,noise power,signal processing,covariance matrix,maximum likelihood estimation,stochastic resonance
Applied mathematics,Signal processing,Array processing,Noise power,Control theory,Sensor array,Stochastic process,Covariance matrix,Estimation theory,Statistics,Mathematics,Estimator
Journal
Volume
Issue
ISSN
44
1
1053-587X
Citations 
PageRank 
References 
33
2.71
7
Authors
4
Name
Order
Citations
PageRank
P. Stoica174156.84
Björn E. Ottersten26418575.28
M. Viberg3917188.13
R.L. Moses4748.03