Sensor array processing based on subspace fitting

Algorithms for estimating unknown signal parameters from the measured output of a sensor array are considered in connection with the subspace fitting problem. The methods considered are the deterministic maximum likelihood method (ML), ESPRIT, and a recently proposed multidimensional signal subspace...

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Bibliographic Details
Published in:IEEE transactions on signal processing Vol. 39; no. 5; pp. 1110 - 1121
Main Authors: Viberg, M., Ottersten, B.
Format: Journal Article
Language:English
Published: IEEE 01.05.1991
Subjects:
Array signal processing
Automatic control
Direction of arrival estimation
Fitting
Geophysical measurements
Information technology
Informationsteknik
Maximum likelihood estimation
Multidimensional systems
Parameter estimation
Processing
Reglerteknik
Sensor array
Sensor arrays
Signal processing
Signal processing algorithms
TECHNOLOGY
TEKNIKVETENSKAP
ISSN:1053-587X, 1941-0476
Online Access:Get full text
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Summary:Algorithms for estimating unknown signal parameters from the measured output of a sensor array are considered in connection with the subspace fitting problem. The methods considered are the deterministic maximum likelihood method (ML), ESPRIT, and a recently proposed multidimensional signal subspace method. These methods are formulated in a subspace-fitting-based framework, which provides insight into their algebraic and asymptotic relations. It is shown that by introducing a specific weighting matrix, the multidimensional signal subspace method can achieve the same asymptotic properties as the ML method. The asymptotic distribution of the estimation error is derived for a general subspace weighting, and the weighting that provides minimum variance estimates is identified. The resulting optimal technique is termed the weighted subspace fitting (WSF) method. Numerical examples indicate that the asymptotic variance of the WSF estimates coincides with the Cramer-Rao bound. The performance improvement compared to the other techniques is found to be most prominent for highly correlated signals.< >
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ISSN:1053-587X
1941-0476
DOI:10.1109/78.80966