Stochastic maximum likelihood estimation in sensor arrays by weighted subspace fitting

Ottersten, BjörnInformation Systems Lab, Stanford University, Stanford, CA 94305, USA (author)

Sponsors: Naval Postgraduate Sch, Monterey, CA, USA; San Jose Statae Univ, San Jose, CA, USA; IEEE Computer Soc, Los Alamitos, CA, USA. QC 20120102
NR 20140805
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.

Wahlberg, Bo,1959-Linköpings universitet,Reglerteknik,Tekniska högskolan,Department of Electrical Engineering, Linköping University, S-581 83 Linköping, Sweden,Automatic Control(Swepub:kth)u18c1tfa (author)
Viberg, M.Linköpings universitet,Reglerteknik,Tekniska högskolan,Department of Electrical Engineering, Linköping University, S-581 83 Linköping, Sweden,Automatic Control (author)
Kailath, ThomasInformation Systems Lab, Stanford University, Stanford, CA 94305, USA (author)
Information Systems Lab, Stanford University, Stanford, CA 94305, USAReglerteknik (creator_code:org_t)

In:Conference Record - Asilomar Conference on Circuits, Systems & ComputersPacific Grove, CA, USA : Linköping University, s. 599-603, s. 599-603
In:Proceedings of the 23rd Asilomar Conference on Signals, Systems and ComputersPacific Grove, CA, USA : Linköping University, s. 599-603, s. 599-603

By the author/editor: Ottersten, Björn; Wahlberg, Bo, 19 ...; Viberg, M.; Kailath, Thomas

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