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Stochastic ML Estimation in Sensor Arrays by Weighted Subspace Fitting
Stanford University, CA, USA.
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, CA, USA.
1989 (English)In: Proceedings of the 23rd Asilomar Conference on Signals, Systems and Computers, 1989, 599-603 p.Conference paper, Published paper (Refereed)
Abstract [en]

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
1989. 599-603 p.
Keyword [en]
Probability, Sensors, Cramer-Rao Bound, Maximum Likelihood Estimation, Weighted Subspace Fitting, Control Systems
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-100552DOI: 10.1109/ACSSC.1989.1200968ISBN: 0-929029-30-1 (print)OAI: oai:DiVA.org:liu-100552DiVA: diva2:663069
Conference
23rd Asilomar Conference on Signals, Systems and Computers, November, 1989, Monterey, USA
Available from: 2013-11-09 Created: 2013-11-09 Last updated: 2014-02-06

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