# Deep Stochastic Radar Models

arXiv: https://arxiv.org/abs/1701.09180 · HTML: https://mlsanity.com/paper/1701.09180/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2017.

## Built on by (most cited first, 9 of 9)

- [Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2304.10410/) (2023)
- [RadSimReal: Bridging the Gap Between Synthetic and Real Data in Radar Object Detection With Simulation](https://mlsanity.com/paper/2404.18150/) (2024)
- [Generation of Realistic Synthetic Raw Radar Data for Automated Driving Applications using Generative Adversarial Networks](https://mlsanity.com/paper/2308.02632/) (2023)
- [RF4D:Neural Radar Fields for Novel View Synthesis in Outdoor Dynamic Scenes](https://mlsanity.com/paper/2505.20967/) (2025)
- [Controllable Radar Simulation with Waveform Parameter Embedding](https://mlsanity.com/paper/2506.03134/) (2025)
- [RadarGen: Automotive Radar Point Cloud Generation from Cameras](https://mlsanity.com/paper/2512.17897/) (2025)
- [Sensor Visibility Estimation: Metrics and Methods for Systematic Performance Evaluation and Improvement](https://mlsanity.com/paper/2211.06308/) (2022)
- [Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining](https://mlsanity.com/paper/2501.09597/) (2025)
- [Integrated Detection and Tracking Based on Radar Range-Doppler Feature](https://mlsanity.com/paper/2509.06569/) (2025)

## Abstract (arXiv)

Accurate simulation and validation of advanced driver assistance systems requires accurate sensor models. Modeling automotive radar is complicated by effects such as multipath reflections, interference, reflective surfaces, discrete cells, and attenuation. Detailed radar simulations based on physical principles exist but are computationally intractable for realistic automotive scenes. This paper describes a methodology for the construction of stochastic automotive radar models based on deep learning with adversarial loss connected to real-world data. The resulting model exhibits fundamental radar effects while remaining real-time capable.