Ocean Vector Wind Retrieval from Delay-Doppler Maps using Ambiguous Stare Processing

Ian Collett and Y. Jade Morton

Peer Reviewed

Abstract:	In GNSS reflectometry, GNSS signals reflected from the surface of the Earth are used for environmental remote sensing, such as estimating ocean surface winds. In this context, wind speed retrieval algorithms have been developed and are in operational use. These algorithms utilize a data product called the delay-Doppler map (DDM), a measure of the received scattered signal power. Of course, measuring ocean surface vector winds also requires knowledge of the wind direction, which has proven to be a far more difficult target. A technique called ambiguous stare processing, which follows a number of fixed points on the surface as they propagate through a sequence of DDMs, has exhibited a sensitivity to wind direction. However, a full vector wind retrieval algorithm has not yet been established. In this paper, such an algorithm is developed and tested using sequences of DDMs simulated for a spaceborne receiver in low-Earth orbit. Samples of the normalized bistatic radar cross-section are taken from the DDMs and compared to a model to simultaneously retrieve the wind speed and direction. The algorithm performance in terms of the wind direction retrieval RMS error is reported for different choices of the sampling scheme and the quantity of DDMs used. The most favorable result shows that the wind direction can be retrieved under the presence of noise with an RMS error of 30 degrees, while maintaining a spatial resolution better than 50 km.
Published in:	Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019) September 16 - 20, 2019 Hyatt Regency Miami Miami, Florida
Pages:	3309 - 3318
Cite this article:	Collett, Ian, Morton, Y. Jade, "Ocean Vector Wind Retrieval from Delay-Doppler Maps using Ambiguous Stare Processing," Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019), Miami, Florida, September 2019, pp. 3309-3318. https://doi.org/10.33012/2019.17101
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