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Analysis of Algorithms for Sensor Arrays with Invariance Structure
Stanford University, CA, USA.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Stanford University, CA, USA.
1990 (English)In: Proceedings of the 1990 International Conference on Acoustics, Speech and Signal Processing, 1990, 2959-2962 p.Conference paper (Refereed)
Abstract [en]

The problem of estimating signal parameters from sensor array data is addressed. If the array is composed of two identical subarrays, (i.e. one invariance) the ESPRIT algorithm is known to yield parameter estimates in a very cost efficient manner. Recently, the total least squares (TLS) version of ESPRIT has been formulated in a subspace fitting framework. In this formulation, the ESPRIT concept is easily generalized to arrays exhibiting more than one invariance. The asymptotic properties for this class of algorithms are derived. The estimates are shown to be statistically efficient under certain assumptions. The case of a uniform linear array is studied in more detail, and a generalization of the ESPRIT algorithm is proposed by introducing row weighting of the subspace estimate.

Place, publisher, year, edition, pages
1990. 2959-2962 p.
Keyword [en]
Parameter estimation, Signal processing, Sensor arrays, Invariance structure
National Category
Control Engineering
URN: urn:nbn:se:liu:diva-100559DOI: 10.1109/ICASSP.1990.116247OAI: diva2:663075
1990 International Conference on Acoustics, Speech and Signal Processing, 3-6 April, 1990, Albuquerque, NM, USA
Available from: 2013-11-09 Created: 2013-11-09 Last updated: 2014-02-06

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ReferencesLink to record
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