Near-field multiple source localization by passive sensor array

The localization of multiple near-field sources in a spatially white Gaussian noise environment is studied. A modified two-dimensional (2-D) version of the multiple signal classification (MUSIC) algorithm is used to localize the signal sources; range and bearing. A global-optimum maximum likelihood...

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Bibliographic Details
Published inIEEE transactions on antennas and propagation Vol. 39; no. 7; pp. 968 - 975
Main Authors Huang, Y.-D., Barkat, M.
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.07.1991
Institute of Electrical and Electronics Engineers
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Summary:The localization of multiple near-field sources in a spatially white Gaussian noise environment is studied. A modified two-dimensional (2-D) version of the multiple signal classification (MUSIC) algorithm is used to localize the signal sources; range and bearing. A global-optimum maximum likelihood searching approach to localize these sources is discussed. It is shown that in the single source situation, the covariances of both the 2-D MUSIC estimator and the maximum likelihood estimator (MLE) approach the Cramer-Rao lower bound as the number of snapshots increases to infinity. In the multiple source situation, it is observed that for a high signal-to-noise ratio (SNR) and a large number of snapshots, the root mean square errors (RMSEs) of both localization techniques are relatively small. However, for low SNR and/or small number of snapshots, the performance of the MLE is much superior that of the modified 2-D MUSIC.< >
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content type line 23
ISSN:0018-926X
1558-2221
DOI:10.1109/8.86917