Efficient DOA estimation using signal eigenvector method without eigendecomposition

In this paper, we propose the new method of SEM (signal eigenvector method) using the block Lanczos algorithm for improving the resolution in bearing angle estimation of narrowband coherent signals at a uniform linear array. The signal eigenvector method uses the signal subspace that is composed of...

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Main Author: Kyungseok Kim
Format: Conference Proceeding
Language:English
Subjects:
Direction of arrival estimation
Eigenvalues and eigenfunctions
Gaussian noise
Narrowband
Phased arrays
Propagation delay
Sensor arrays
Signal processing algorithms
Signal resolution
Working environment noise
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description In this paper, we propose the new method of SEM (signal eigenvector method) using the block Lanczos algorithm for improving the resolution in bearing angle estimation of narrowband coherent signals at a uniform linear array. The signal eigenvector method uses the signal subspace that is composed of the eigenvectors associated with the largest eigenvalues of the array spatial correlation matrix. The proposed method estimates the signal subspace using the block Lanczos algorithm instead of eigendecomposition. The results of computer simulation are shown the superior performance of this proposed method to the conventional SEM under narrowband coherent signal environment.
doi_str_mv 10.1109/ICUPC.1998.733051
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IEEE 1998 International Conference on Universal Personal Communications. Conference Proceedings (Cat. No.98TH8384)</btitle><stitle>ICUPC</stitle><date>1998</date><risdate>1998</risdate><volume>1</volume><spage>661</spage><epage>664 vol.1</epage><pages>661-664 vol.1</pages><issn>1091-8442</issn><isbn>0780351061</isbn><isbn>9780780351066</isbn><abstract>In this paper, we propose the new method of SEM (signal eigenvector method) using the block Lanczos algorithm for improving the resolution in bearing angle estimation of narrowband coherent signals at a uniform linear array. The signal eigenvector method uses the signal subspace that is composed of the eigenvectors associated with the largest eigenvalues of the array spatial correlation matrix. The proposed method estimates the signal subspace using the block Lanczos algorithm instead of eigendecomposition. 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subjects Direction of arrival estimation
Eigenvalues and eigenfunctions
Gaussian noise
Narrowband
Phased arrays
Propagation delay
Sensor arrays
Signal processing algorithms
Signal resolution
Working environment noise
title Efficient DOA estimation using signal eigenvector method without eigendecomposition
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