Stochastic maximum likelihood estimation in sensor arrays by weighted subspace fitting
1989 (English)In: Conference Record - Asilomar Conference on Circuits, Systems & Computers: Volume 2 / [ed] Ray R. Chen, Pacific Grove, CA, USA, 1989, Vol. 2, no San Jose, CA, United States, 599-603 p.Conference paper (Refereed)
The problem of estimating parameters of multiple narrowband emitter signals from sensor array data is considered. Under the assumption of Gaussian distributed emitter signals, the stochastic maximum-likelihood (ML) technique is known to provide statistically efficient estimates, i.e., it achieves the Cramer-Rao bound (CRB). A multidimensional signal subspace method, termed weighted subspace fitting (WSF), has recently been proposed. It is shown that the WSF and ML estimates are asymptotically identical (for large data records). As a consequence, the WSF method is asymptotically efficient, assuming temporally white Gaussian signal waveforms and noise.
Place, publisher, year, edition, pages
Pacific Grove, CA, USA, 1989. Vol. 2, no San Jose, CA, United States, 599-603 p.
Probability, Sensors, Cramer-Rao Bound, Maximum Likelihood Estimation, Weighted Subspace Fitting, Control Systems
IdentifiersURN: urn:nbn:se:kth:diva-55448OAI: oai:DiVA.org:kth-55448DiVA: diva2:471571
Twenty-Third Annual Asilomar Conference on Signals, Systems & Computers, Pacific Grove, CA, USA, 30 October - 1 November, 1989
Sponsors: Naval Postgraduate Sch, Monterey, CA, USA; San Jose Statae Univ, San Jose, CA, USA; IEEE Computer Soc, Los Alamitos, CA, USA. QC 201201022012-01-022012-01-022013-09-05Bibliographically approved