Maximum Likelihood Array Processing in Spatially Correlated Noise Fields Using Parametrized Signals
1997 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, Vol. 45, no 4, 996-1004 p.Article in journal (Refereed) Published
This paper deals with the problem of estimating signal parameters using an array of sensors. This problem is of interest in a variety of applications, such as radar and sonar source localization. A vast number of estimation techniques have been proposed in the literature during the past two decades. Most of these can deliver consistent estimates only if the covariance matrix of the background noise is known. In many applications, the aforementioned assumption is unrealistic. Recently, a number of contributions have addressed the problem of signal parameter estimation in unknown noise environments based on various assumptions on the noise. Herein, a different approach is taken. We assume instead that the signals are partially known. The received signals are modeled as linear combinations of certain known basis functions. The exact maximum likelihood (ML) estimator for the problem at hand is derived, as well as computationally more attractive approximation. The Cramer-Rao lower bound (CRB) on the estimation error variance is also derived and found to coincide with the CRB, assuming an arbitrary deterministic model and known noise covariance.
Place, publisher, year, edition, pages
1997. Vol. 45, no 4, 996-1004 p.
Array signal processing, Background noise, Covariance matrix, Estimation error, Maximum likelihood estimation, Parameter estimation, Radar applications, Sensor arrays, Sonar applications, Working environment noise
IdentifiersURN: urn:nbn:se:kth:diva-86936DOI: 10.1109/78.564187OAI: oai:DiVA.org:kth-86936DiVA: diva2:501192
NR 201408052012-02-142012-02-142012-02-14Bibliographically approved