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Analysis of Subspace Fitting and ML Techniques for Parameter Estimation from Sensor Array Data
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Stanford University, USA.
1992 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 40, no 3, 590-600 p.Article in journal (Refereed) Published
Abstract [en]

It is shown that the multidimensional signal subspace method, termed weighted subspace fitting (WSF), is asymptotically efficient. This results in a novel, compact matrix expression for the Cramer-Rao bound (CRB) on the estimation error variance. The asymptotic analysis of the maximum likelihood (ML) and WSF methods is extended to deterministic emitter signals. The asymptotic properties of the estimates for this case are shown to be identical to the Gaussian emitter signal case, i.e. independent of the actual signal waveforms. Conclusions concerning the modeling aspect of the sensor array problem are drawn.

Place, publisher, year, edition, pages
1992. Vol. 40, no 3, 590-600 p.
Keyword [en]
Detectors, Matrix algebra, Parameter estimation, Signal processing
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-95635DOI: 10.1109/78.120802OAI: oai:DiVA.org:liu-95635DiVA: diva2:637082
Available from: 2013-07-16 Created: 2013-07-16 Last updated: 2017-12-06

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