Direction Finding and Likelihood Ratio Detection for Oceanographic HF Radars
Previous work with simulations of oceanographic high-frequency (HF) radars has identified possible improvements when using maximum likelihood estimation (MLE) for direction of arrival; however, methods for determining the number of emitters (here defined as spatially distinct patches of the ocean su...
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Published in | Journal of atmospheric and oceanic technology Vol. 39; no. 2; pp. 223 - 235 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Boston
American Meteorological Society
01.02.2022
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Subjects | |
Online Access | Get full text |
ISSN | 0739-0572 1520-0426 |
DOI | 10.1175/JTECH-D-21-0110.1 |
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Summary: | Previous work with simulations of oceanographic high-frequency (HF) radars has identified possible improvements when using maximum likelihood estimation (MLE) for direction of arrival; however, methods for determining the number of emitters (here defined as spatially distinct patches of the ocean surface) have not realized these improvements. Here we describe and evaluate the use of the likelihood ratio (LR) for emitter detection, demonstrating its application to oceanographic HF radar data. The combined detection–estimation methods MLE-LR are compared with multiple signal classification method (MUSIC) and MUSIC parameters for SeaSonde HF radars, along with a method developed for 8-channel systems known as MUSIC-Highest. Results show that the use of MLE-LR produces similar accuracy, in terms of the RMS difference and correlation coefficients squared, as previous methods. We demonstrate that improved accuracy can be obtained for both methods, at the cost of fewer velocity observations and decreased spatial coverage. For SeaSondes, accuracy improvements are obtained with less commonly used parameter sets. The MLE-LR is shown to be able to resolve simultaneous closely spaced emitters, which has the potential to improve observations obtained by HF radars operating in complex current environments. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
ISSN: | 0739-0572 1520-0426 |
DOI: | 10.1175/JTECH-D-21-0110.1 |