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 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
IEEE
01.10.2002
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0018-926X, 1558-2221 |
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| Abstract | 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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| AbstractList | 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. 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. (Author) |
| Author | McCloud, M.L. Scharf, L.L. |
| Author_xml | – sequence: 1 givenname: M.L. surname: McCloud fullname: McCloud, M.L. organization: Magis Networks Inc., San Diego, CA, USA – sequence: 2 givenname: L.L. surname: Scharf fullname: Scharf, L.L. |
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| Cites_doi | 10.1109/icassp.1993.319673 10.1109/29.17564 10.1137/S0895479892230523 10.1109/7.62249 10.1137/S0895479891224245 10.1016/S0090-5267(96)80030-2 10.1109/78.286957 |
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| SubjectTerms | 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 |
| Title | A new subspace identification algorithm for high-resolution DOA estimation |
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