Neural Posterior Estimation of Terrain Parameters from Radar Sounder Data
Radar sounders are electromagnetic instruments that can probe deep into the subsurface of Earth and other planetary bodies by processing the echo of transmitted radar waves. Conventional approaches for analyzing such data rely on approximate assumptions and often produce point estimates that ignore parameter correlations as well as galactic and measurement noise. We propose a simulation-based inference approach to terrain parameter inversion from radar sounder data, where synthetic observations from a GPU-based simulator are used to train a neural network-based density estimator for neural posterior estimation (NPE). By explicitly conditioning on reference surface assumptions, the propose...
| Authors | Jordy Dal Corso, Annalena Kofler, Marco Cortellazzi et al. |
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| Primary category | eess.SP |
| Published | 5 May 2026 |
| Last updated at source | 23 June 2026 |
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