'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
Global navigation satellite system reflectometry (GNSS-R) is a group of techniques that uses satellite navigation signals as signals of opportunity for remote sensing applications. In GNSS-R, large amounts of data are acquired and have to be processed. Computation time is typically the bottleneck for ground and airborne experiments. This article presents an efficient solution for off-line GNSS-R processing data taking advantage of graphics processing units (GPUs). After comparing to the typically used CPU languages, such as MATLAB and C++, the advantage of using parallel processing on the GPU is clear. GPU-based computation can reduce the processing time by as much as 95% of the acquisition time of the data. An implementation taking advantage of a home-use GPU is proposed for the data processing units. Thanks to its efficiency, even real-time processing experiments are feasible.This work was supported in part bythe Spanish Ministry of Economy and Competitiveness and FEDER EU underthe project “AGORA: Técnicas Avanzadas en Teledetección Aplicada UsandoSeñales GNSS y Otras Señales de Oportunidad” (MINECO/FEDER) ESP2015-70014-C2-1-R by the Agencia Estatal de Investigación; in part by the SpanishMinistry of Science, Innovation, and Universities, “Sensing with Pioneering Opportunistic Techniques,” under Grant RTI2018-099008-B-C21; in part by the Collaboration under Grant 998142 by the Spanish Ministry of Education; inpart by Unidad de Excelencia María de Maeztu MDM-2016-0600; and in partby Prof. Camps ICREA Academia 2015 award of the Generalitat de Catalunya.Peer ReviewedPostprint (published version
