Analysis of Subspace Fitting and ML Techniques for Parameter Estimation from Sensor Array Data
1989 (English)Report (Other academic)
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
Linköping: Linköping University , 1989. , 33 p.
LiTH-ISY-I, ISSN 8765-4321 ; 1029
Detectors, Matrix algebra, Parameter estimation, Signal processing
IdentifiersURN: urn:nbn:se:liu:diva-103967OAI: oai:DiVA.org:liu-103967DiVA: diva2:693350