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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Vydáno v:IEEE transactions on antennas and propagation Ročník 50; číslo 10; s. 1382 - 1390
Hlavní autoři: McCloud, M.L., Scharf, L.L.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.10.2002
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Arrays
Direction of arrival estimation
Energy resolution
Estimators
Length measurement
Mathematical models
Multiple signal classification
Music
Perturbation methods
Representations
Sensor arrays
Signal resolution
Signal to noise ratio
Standards development
Stochastic processes
Subspaces
Vectors
ISSN:0018-926X, 1558-2221
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Shrnutí: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