@inproceedings{
kohler2023symmetric,
title={Symmetric Models for Radar Response Modeling},
author={Colin Kohler and Nathan Vaska and Ramya Muthukrishnan and Whangbong Choi and Jung Yeon Park and Justin Goodwin and Rajmonda Caceres and Robin Walters},
booktitle={NeurIPS 2023 Workshop on Symmetry and Geometry in Neural Representations},
year={2023},
url={https://openreview.net/forum?id=9G0e8QrpxP}
}
Many radar applications require complex radar signature models that incorporate characteristics of an object's shape and dynamics as well as sensing effects. Even though high-fidelity, first-principles radar simulators are available, they tend to be resource-intensive and do not easily support the requirements of agile and large-scale AI development and evaluation frameworks. Deep learning represents an attractive alternative to these numerical methods, but can have large data requirements and limited generalization ability. In this work, we present the Radar Equivariant Model (REM), the first $SO(3)$-equivaraint model for predicting radar responses from object meshes. By constraining our model to the symmetries inherent to radar sensing, REM is able to achieve a high level reconstruction of signals generated by a first-principles radar model and shows improved performance and sample efficiency over other encoder-decoder models.
Symmetric Models for Radar Response Modeling
Colin Kohler Nathan Vaska Ramya Muthukrishnan Whangbong Choi Jung Yeon Park Justin Goodwin Rajmonda Caceres Robin Walters