Empirical Covariance Models for Sequential Processing of GNSS-R Delay Doppler Maps

Kevin Shi and James L. Garrison

Abstract:	Delay Doppler Maps (DDMs) generated from spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) contain valuable information on surface characteristics of the Earth that can be exploited for various applications in remote sensing, one of which is the estimation of ocean surface winds. DDMs generated by the Cyclone Global Navigation Satellite System (CYGNSS) contain information on regions of the ocean surface spanning an area over 90 km by 90 km. Techniques developed for processing a time series of DDMs, either through model assimilation or sequential estimation, can take advantage of the information shared between multiple DDMs corresponding to overlapping areas on the ocean surface. Optimal processing of each DDM requires a model for the covariance matrix. We present an empirical covariance model for the DDM incorporating both thermal and speckle noise effects and representing correlation between observations at different Doppler and delay bins (i.e. fast time) and the correlation between sequential DDMs in time (e.g. slow time). The empirical model in fast time is found to depend only on the ocean surface wind speed and the power in each DDM bin. We show that there is a correlation between consecutive DDMs, with a correlation time that increases with decreasing ocean surface wind speed. For wind speeds higher than 7 m/s, this correlation time reduces to zero.
Published in:	Proceedings of the ION 2024 Pacific PNT Meeting April 15 - 18, 2024 Hilton Waikiki Beach Honolulu, Hawaii
Pages:	1 - 10
Cite this article:	Shi, Kevin, Garrison, James L., "Empirical Covariance Models for Sequential Processing of GNSS-R Delay Doppler Maps," Proceedings of the ION 2024 Pacific PNT Meeting, Honolulu, Hawaii, April 2024, pp. 1-10. https://doi.org/10.33012/2024.19666
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