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ANALYSIS OF SUBSPACE FITTING AND ML TECHNIQUES FOR PARAMETER-ESTIMATION FROM SENSOR ARRAY DATA
KTH, School of Electrical Engineering (EES), Signal Processing. (Signal Processing)ORCID iD: 0000-0003-2298-6774
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]

Signal parameter estimation from sensor array data is a problem that is encountered in many engineering applications. Under the assumption of Gaussian distributed emitter signals, the so-called stochastic maximum likelihood (ML) technique is known to be statistically efficient, i.e., the estimation error covariance attains the Cramer-Rao bound (CRB) asymptotically. Herein, it is shown that also the multi-dimensional signal subspace method, termed weighted subspace fitting (WSF), is asymptotically efficient. This also results in a novel, compact matrix expression for the CRB on the estimation error variance. The asymptotic analysis of the 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.
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-60034DOI: 10.1109/78.120802ISI: A1992HF01600010OAI: oai:DiVA.org:kth-60034DiVA: diva2:477136
Note
NR 20140805Available from: 2012-01-12 Created: 2012-01-12 Last updated: 2017-12-08Bibliographically approved

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Ottersten, Björn

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