Differentiable Point Scattering Models for Efficient Radar Target Characterization

TL;DR

This paper introduces differentiable radar scattering models that enable efficient, gradient-based optimization for accurate target characterization in defense applications.

Contribution

The work presents a new class of radar models that are both high-fidelity and computationally efficient, facilitating faster target parameter estimation.

Findings

Models enable gradient-based optimization

Improved efficiency in target characterization

Potential for real-time radar analysis

Abstract

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.