Symmetric Models for Radar Response Modeling
Abstract
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.
Cite
Text
Kohler et al. "Symmetric Models for Radar Response Modeling." NeurIPS 2023 Workshops: NeurReps, 2023.Markdown
[Kohler et al. "Symmetric Models for Radar Response Modeling." NeurIPS 2023 Workshops: NeurReps, 2023.](https://mlanthology.org/neuripsw/2023/kohler2023neuripsw-symmetric/)BibTeX
@inproceedings{kohler2023neuripsw-symmetric,
title = {{Symmetric Models for Radar Response Modeling}},
author = {Kohler, Colin and Vaska, Nathan and Muthukrishnan, Ramya and Choi, Whangbong and Park, Jung Yeon and Goodwin, Justin and Caceres, Rajmonda and Walters, Robin},
booktitle = {NeurIPS 2023 Workshops: NeurReps},
year = {2023},
url = {https://mlanthology.org/neuripsw/2023/kohler2023neuripsw-symmetric/}
}