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Asymptotic Results for Multidimensional Sensor Array Processing
Stanford University, CA, USA.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Stanford University, CA, USA.
1988 (English)In: Proceedings of the 22nd Asilomar Conference on Signals, Systems and Computers, 1988, 833-837 p.Conference paper (Refereed)
Abstract [en]

The problem of estimating signal parameters from the output of a sensor array is adressed. The Maximum Likelihood estimation procedure is a systematic approach to many parameter estimation problems. The deterministic ML method has been formulated and several methods for maximizing the cos function have been proposed. However, the asymptotic distribution of the estimation error has not ben reported for the general case. The distribution will be derived in this paper. Multidimensional signal subspace methods have been proposed recently with advantages over conventional one-dimensional subspace methods. We examine the asymptotic properties of a weighted subspace fitting problem. A weighting matrix is proposed which gives the subspace fitting method the same asymptotic distribution as the ML method.

Place, publisher, year, edition, pages
1988. 833-837 p.
Keyword [en]
Multidimensional sensor array processing, Asymptotic distribution, Maximum Likelihood estimation
National Category
Control Engineering
URN: urn:nbn:se:liu:diva-100581DOI: 10.1109/ACSSC.1988.754666OAI: diva2:663117
22nd Asilomar Conference on Signals, Systems and Computers, November, 1988, Monterey, USA
Available from: 2013-11-09 Created: 2013-11-09 Last updated: 2014-02-08

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ReferencesLink to record
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