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  • 1
    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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  • 2
    Publication Date: 2022-05-26
    Description: Author Posting. © American Meteorological Society, 2019. 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 36(10), (2019): 1997-2014, doi: 10.1175/JTECH-D-19-0029.1.
    Description: While land-based high-frequency (HF) radars are the only instruments capable of resolving both the temporal and spatial variability of surface currents in the coastal ocean, recent high-resolution views suggest that the coastal ocean is more complex than presently deployed radar systems are able to reveal. This work uses a hybrid system, having elements of both phased arrays and direction finding radars, to improve the azimuthal resolution of HF radars. Data from two radars deployed along the U.S. East Coast and configured as 8-antenna grid arrays were used to evaluate potential direction finding and signal, or emitter, detection methods. Direction finding methods such as maximum likelihood estimation generally performed better than the well-known multiple signal classification (MUSIC) method given identical emitter detection methods. However, accurately estimating the number of emitters present in HF radar observations is a challenge. As MUSIC’s direction-of-arrival (DOA) function permits simple empirical tests that dramatically aid the detection process, MUSIC was found to be the superior method in this study. The 8-antenna arrays were able to provide more accurate estimates of MUSIC’s noise subspace than typical 3-antenna systems, eliminating the need for a series of empirical parameters to control MUSIC’s performance. Code developed for this research has been made available in an online repository.
    Description: This analysis was supported by NSF Grants OCE-1657896 and OCE-1736930 to Kirincich, OCE-1658475 to Emery and Washburn and OCE-1736709 to Flament. Flament is also supported by NOAA’s Integrated Ocean Observing System through Award NA11NOS0120039. The authors thank Lindsey Benjamin, Alma Castillo, Ken Constantine, Benedicte Dousset, Ian Fernandez, Mael Flament, Dave Harris, Garrett Hebert, Ben Hodges, Victoria Futch, Matt Guanci, and Philip Moravcik for assistance in building, deploying, and operating the radars.
    Description: 2020-04-11
    Keywords: Ocean ; Coastal flows ; Algorithms ; Radars/Radar observations ; Remote sensing
    Repository Name: Woods Hole Open Access Server
    Type: Article
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