Publication Date:
2022-09-15
Description:
Author Posting. © American Meteorological Society, 2022. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of the Atmospheric and Oceanic Technology 39(2), (2022): 223–235, https://doi.org/10.1175/JTECH-D-21-0110.1.
Description:
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.
Description:
This work was supported by the National Science Foundation (NSF) under Grant OCE-1658475. Computing resources were provided by the UCSB Center for Scientific Computing through an NSF MRSEC (DMR-1720256) and NSF CNS-1725797.
Keywords:
Ocean
;
Algorithms
;
Data quality control
;
Radars/radar observations
;
Remote sensing
;
Surface observations
;
Quality assurance/control
Repository Name:
Woods Hole Open Access Server
Type:
Article
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