A new subspace identification algorithm for high-resolution DOA estimation

In this paper, we propose a new direction of arrival (DOA) estimator for sensor-array processing. The estimator is based on a linear algebraic connection between the standard subspace model of the array correlation matrix and a special signal-plus-interference model, which we develop in this paper....

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Bibliographic Details
Published in:IEEE transactions on antennas and propagation Vol. 50; no. 10; pp. 1382 - 1390
Main Authors: McCloud, M.L., Scharf, L.L.
Format: Journal Article
Language:English
Published: New York IEEE 01.10.2002
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN:0018-926X, 1558-2221
Online Access:Get full text
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Summary:In this paper, we propose a new direction of arrival (DOA) estimator for sensor-array processing. The estimator is based on a linear algebraic connection between the standard subspace model of the array correlation matrix and a special signal-plus-interference model, which we develop in this paper. The estimator we propose is a signal subspace scaled MUSIC algorithm, which we call SSMUSIC. It is not a subspace weighted MUSIC, because the scaling depends on the eigenstructure of the estimated signal subspace. SSMUSIC has the advantage of simultaneously estimating the DOA and the power of each source. We employ a second-order perturbation analysis of the estimator and derive stochastic representations for its bias and squared-error. We compare the new DOA estimator with the MUSIC estimator, based on these representations. Numerical results demonstrate the superior performance of SSMUSIC relative to MUSIC and the validity of the perturbation results.
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ISSN:0018-926X
1558-2221
DOI:10.1109/TAP.2002.805244