An Approach to Quick-Look Analysis of Radar Sounder Data Acquired in Planetary Missions

Radar sounders have become crucial instruments for subsurface exploration in planetary missions, providing insights into the internal structure and composition of celestial bodies. This article presents a novel automatic framework for the quick-look analysis (QLA) of radar sounder data acquired in space missions. The methodology serves the dual purpose of supporting both data science analysis and operational planning. By integrating standard and novel processing techniques and evaluation metrics, the framework provides automated assessment of signal quality, early detection of instrument anomalies, and preliminary identification of surface and subsurface features. Initially developed for the radar for icy moon exploration (RIME) instrument onboard Jupiter Icy Moons Explorer (JUICE) mission of the European Space Agency (ESA), it is designed to be adaptable to other planetary radar sounders, offering a scalable approach to future space exploration missions. The proposed methodology has been experimentally validated using the RIME end-to-end simulator, through a comprehensive set of simulations based on realistic acquisition scenarios representative of different mission phases and target environments.