Maximum Likelihood Array Processing for Stochastic Coherent Sources
1996 (English)In: In IEEE Trans. on Signal Processing, ISSN 1053-587X, Vol. 44, no 1, 96-105 p.Article in journal (Refereed) Published
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.
Array signal processing, Covariance matrix, Maximum likelihood detection, Maximum likelihood estimation, Parameter estimation, Sensor arrays, Signal processing, Stochastic processes, Stochastic resonance, Yield estimation
IdentifiersURN: urn:nbn:se:kth:diva-86950DOI: 10.1109/78.482015OAI: oai:DiVA.org:kth-86950DiVA: diva2:501209
NR 201408052012-02-142012-02-142012-02-14Bibliographically approved