The Global Navigation Satellite Systems Reflectometry (GNSS-R) Microwave Interferometric Reflectometer: Hardware, Calibration, and Validation Experiments

Onrubia, R.; Pascual, Daniel; Querol, Jorge; Park, Hyuk; Camps, A.

doi:10.3390/s19051019

Cited by 12 publications

(16 citation statements)

References 13 publications

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“…The MIR instrument attempted to track a GPS and a Galileo satellite in each band whenever possible. In case this was not possible, the two satellites with the highest elevation angle were chosen [6].…”

Section: Methodsmentioning

confidence: 99%

“…The received data is sampled by pairs of up-and down-looking beams synchronously at 1 bit for the I and Q components at 32,736 Msps, and stored for later post-processing. A detailed description of the instrument can be found in [6]. The instrument was mounted in a research airplane (see Figure 1).…”

Section: Instrument and Field Campaign Descriptionmentioning

confidence: 99%

“…This work analyzes the satellite cross-talk that affected four flights of the microwave interferometric reflectometer (MIR) instrument [6]. First, Section 2 briefly describes the MIR instrument and the field campaign.…”

Section: Introductionmentioning

confidence: 99%

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Satellite Cross-Talk Impact Analysis in Airborne Interferometric Global Navigation Satellite System-Reflectometry with the Microwave Interferometric Reflectometer

et al. 2019

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This work analyzes the satellite cross-talk observed by the microwave interferometric reflectometer (MIR), a new global navigation satellite system (GNSS) reflectometer, during an airborne field campaign in Victoria and New South Wales, Australia. MIR is a GNSS reflectometer with two 19-element, dual-band arrays, each of them having four steerable beams. The data collected during the experiment, the characterization of the arrays, and the global positioning system (GPS) and Galileo ephemeris were used to compute the expected delays and power levels of all incoming signals, and the probability of cross-talk was then evaluated. Despite the MIR highly directive arrays, the largest ever for a GNSS-R instrument, one of the flights was found to be contaminated by cross-talk almost half of the time at the L1/E1 frequency band, and all four flights were contaminated ∼5–10% of the time at the L5/E5a frequency band. The cross-talk introduces an error of up to 40 cm of standard deviation for altimetric applications and about 0.24 dB for scatterometric applications.

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Section: Methodsmentioning

confidence: 99%

Section: Instrument and Field Campaign Descriptionmentioning

confidence: 99%

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Satellite Cross-Talk Impact Analysis in Airborne Interferometric Global Navigation Satellite System-Reflectometry with the Microwave Interferometric Reflectometer

et al. 2019

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“…The goal of this article is to take advantage of GPUs to develop an efficient generic GNSS-R processor and present efficiency results. A full software tool has been developed to process real GNSS-R data, and has been validated with data collected by the microwave interferometric reflectometer (MIR) airborne campaign [6], [7].…”

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confidence: 99%

“…The up-looking array points directly to the GNSS satellites, while the down-looking one points to the corresponding specular reflection points on the earth's surface. The instrument can track up to four different satellites, and their corresponding specular reflection points at L1/E1 (1575.42 MHz) with a bandwith of 20 MHz and L5/E5a (1176.45 MHz) with a bandwith of 34 MHz [6]. During the MIR maiden flight experiments, the signals were down-converted to baseband and sampled at 32.736 MSps at 1 b for both the I and Q components, and then stored in a computer for off-line processing.…”

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confidence: 99%

Advanced GNSS-R Signals Processing With GPUs

Nogues

Pascual

Camps

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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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. Index Terms-Compute unified device architecture (CUDA), global navigation satellite system reflectometry (GNSS-R), graphics processing unit (GPU) processing, parallel computing, real-time processing. I. INTRODUCTION G RAPHICS processing units (GPUs) were initially designed to accelerate graphics computing. However, more recently, they have also been used to accelerate computation times of large amounts of data. In global navigation satellite system reflectometry (GNSS-R), large amounts of data have to be processed either in real-time or offline, and the computation time is a bottleneck. Common programming languages in academia, such as MATLAB [1], are too slow to process large amounts of data. GPU processing has already been applied to some GNSS receiver experiments [2], [3], and it has been used in GNSS-R for

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