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Maximum Likelihood Array Processing for Stochastic Coherent Sources
Department of Systems and Control, Uppsala University, Uppsala, Sweden.
KTH, Superseded Departments, Signals, Sensors and Systems.ORCID iD: 0000-0003-2298-6774
Department of Applied Electronics, Chalmers University of Technology, Gothenburg, Sweden.
Department of Electrical Engineering, The Ohio State University, Columbus, OH 43210 USA..
1996 (English)In: In IEEE Trans. on Signal Processing, ISSN 1053-587X, Vol. 44, no 1, 96-105 p.Article in journal (Refereed) Published
Abstract [en]

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).

Place, publisher, year, edition, pages
IEEE Signal Processing Society, 1996. Vol. 44, no 1, 96-105 p.
Keyword [en]
Array signal processing, Covariance matrix, Maximum likelihood detection, Maximum likelihood estimation, Parameter estimation, Sensor arrays, Signal processing, Stochastic processes, Stochastic resonance, Yield estimation
National Category
Signal Processing
URN: urn:nbn:se:kth:diva-86950DOI: 10.1109/78.482015OAI: diva2:501209
NR 20140805Available from: 2012-02-14 Created: 2012-02-14 Last updated: 2012-02-14Bibliographically approved

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Ottersten, Björn
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Signals, Sensors and Systems
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