# Differentiable Point Scattering Models for Efficient Radar Target Characterization

arXiv: https://arxiv.org/abs/2206.02075 · HTML: https://mlsanity.com/paper/2206.02075/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2022.

## Built on by (most cited first, 1 of 1)

- [NeRF-enabled Analysis-Through-Synthesis for ISAR Imaging of Small Everyday Objects with Sparse and Noisy UWB Radar Data](https://mlsanity.com/paper/2410.10085/) (2024)

## Abstract (arXiv)

Target characterization is an important step in many defense missions, often relying on fitting a known target model to observed data. Optimization of model parameters can be computationally expensive depending on the model complexity, thus having models that both describe the data well and that can be efficiently optimized is critical. This work introduces a class of radar models that can be used to represent the radar scattering response of a target at high frequencies while also enabling the use of gradient-based optimization.