# Classification Of Automotive Targets Using Inverse Synthetic Aperture Radar Images

arXiv: https://arxiv.org/abs/2101.12535 · HTML: https://mlsanity.com/paper/2101.12535/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2021.

## Built on by (most cited first, 4 of 4)

- [Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges](https://mlsanity.com/paper/2308.10407/) (2023)
- [Inverse Airborne Optical Sectioning](https://mlsanity.com/paper/2207.13344/) (2022)
- [Synthetic Aperture Anomaly Imaging](https://mlsanity.com/paper/2304.13590/) (2023)
- [A Dynamic Mode Decomposition Approach to Morphological Component Analysis](https://mlsanity.com/paper/2510.05977/) (2025)

## Abstract (arXiv)

We present a framework for simulating realistic inverse synthetic aperture radar images of automotive targets at millimeter wave frequencies. The model incorporates radar scattering phenomenology of commonly found vehicles along with range-Doppler based clutter and receiver noise. These images provide insights into the physical dimensions of the target, the number of wheels and the trajectory undertaken by the target. The model is experimentally validated with measurement data gathered from an automotive radar. The images from the simulation database are subsequently classified using both traditional machine learning techniques as well as deep neural networks based on transfer learning. We show that the ISAR images offer a classification accuracy above 90% and are robust to both noise and clutter.