An Approach to the Generation and Analysis of Databases of Simulated Radar Sounder Data for Performance Prediction and Target Interpretation

Radar sounders (RSs) are low-frequency instruments that profile the shallow subsurface of planetary targets to obtain valuable scientific information. The prediction of the RS performance and the interpretation of the target properties from the RS data are challenging due to the complex electromagnetic interaction among many acquisition variables. Simulation of RS data can address this issue by modeling the complex interaction and producing simulated radargrams representing the acquisition scenario. In this article, we present an approach to generate databases of geoelectrical models and simulated radargrams corresponding to combinations of: 1) target geoelectrical hypotheses; 2) RS parameters; and 3) acquisition geometry configurations. The proposed approach exploits this database for: 1) predicting the detection performance and sensitivity of the RS and 2) understanding the interpretability of the underlying hypotheses. In order to identify hypothesis combinations that can be unambiguously inverted from the radargrams, we analyze the similarity between pairs of geoelectrical models and between the simulated radargrams, and the statistical distance between radargram features. The approach is demonstrated for the case of Radar for Icy Moons Exploration (RIME), using three selected targets on the Jovian moon Ganymede, with three different simulation techniques. The results are very promising and reveal the effectiveness of the proposed approach in extracting valuable information regarding: 1) the target detection performance of RIME; 2) the sensitivity to the dielectric contrast; 3) the separability of radargram features; and 4) the identification of hypothesis combinations producing significantly different radar response, and thus invertible.