High Performance Simulation of Spaceborne Radar for Remote-Sensing Oceanography: Application to an Altimetry Scenario

TL;DR

This paper presents a GPU-accelerated, high-performance simulator for spaceborne radar in satellite oceanography, capable of efficiently modeling sea surface signals and validating altimetry data processing.

Contribution

The authors introduce a fast simulation mode that significantly reduces computational costs by leveraging assumptions about Doppler spectrum variation, enabling practical large-scale radar data generation.

Findings

Achieved a major speed-up in radar simulation performance.

Validated the simulator with Sentinel-3 SRAL data.

Able to generate sufficient data for SWH and SSH spectra analysis in one day.

Abstract

In this paper, we detail the high-performance implementation of our spaceborne radar simulator for satellite oceanography. Our software simulates the sea surface and the signal to imitate, as far as possible, the measurement process, starting from its lowest level mechanisms. In this perspective, raw data are computed as the sum of many illuminated scatterers, whose time-evolving properties are related to the surface roughness, topography, and kinematics. To achieve efficient performance, we intensively use GPU computing. Moreover, we propose a fast simulation mode based on the assumption that the instantaneous Doppler spectrum within a range gate varies on a timescale significantly larger than the PRI. The sea surface can then be updated at a frequency much smaller than the PRF, drastically reducing the computational cost. When the surface is updated, Doppler spectra are computed for all range gates. Signals segments are then obtained through 1D inverse Fourier transforms and pondered to ensure a smooth time evolution between surface updates. We validate this fast simulation mode with a radar altimeter simulation case of the Sentinel-3 SRAL instrument, showing that simulated raw data can be focused and retrieved using state-of-the-art algorithms. Finally, we show that, using a modest hardware configuration, our simulator can generate enough data in one day to compute the SWH and SSH spectra of a scene. This demonstrate that we achieved an important state-of-the-art speed-up.