# Using Machine Learning to Detect Ghost Images in Automotive Radar

arXiv: https://arxiv.org/abs/2007.05280 · HTML: https://mlsanity.com/paper/2007.05280/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2020.

## Built on by (most cited first, 15 of 15)

- [RadSimReal: Bridging the Gap Between Synthetic and Real Data in Radar Object Detection With Simulation](https://mlsanity.com/paper/2404.18150/) (2024)
- [Radars for Autonomous Driving: A Review of Deep Learning Methods and Challenges](https://mlsanity.com/paper/2306.09304/) (2023)
- [Tackling Clutter in Radar Data -- Label Generation and Detection Using PointNet++](https://mlsanity.com/paper/2303.09530/) (2023)
- [Fast Rule-Based Clutter Detection in Automotive Radar Data](https://mlsanity.com/paper/2108.12224/) (2021)
- [Anomaly Detection in Radar Data Using PointNets](https://mlsanity.com/paper/2109.09401/) (2021)
- [Improving Uncertainty of Deep Learning-based Object Classification on Radar Spectra using Label Smoothing](https://mlsanity.com/paper/2109.12851/) (2021)
- [The Radar Ghost Dataset -- An Evaluation of Ghost Objects in Automotive Radar Data](https://mlsanity.com/paper/2404.01437/) (2024)
- [Investigation of Uncertainty of Deep Learning-based Object Classification on Radar Spectra](https://mlsanity.com/paper/2106.05870/) (2021)
- [BoostRad: Enhancing Object Detection by Boosting Radar Reflections](https://mlsanity.com/paper/2404.17861/) (2024)
- [Edge Case Detection in Automated Driving: Methods, Challenges, and Future Directions](https://mlsanity.com/paper/2410.08491/) (2024)
- [RADLER: Radar Object Detection Leveraging Semantic 3D City Models and Self-Supervised Radar-Image Learning](https://mlsanity.com/paper/2504.12167/) (2025)
- [Revisiting the Evaluation of Deep Neural Networks for Pedestrian Detection](https://mlsanity.com/paper/2511.10308/) (2025)
- [Simultaneous Clutter Detection and Semantic Segmentation of Moving Objects for Automotive Radar Data](https://mlsanity.com/paper/2311.07247/) (2023)
- [Object Modeling from Underwater Forward-Scan Sonar Imagery with Sea-Surface Multipath](https://mlsanity.com/paper/2409.06815/) (2024)
- [Radar-Based NLoS Pedestrian Localization for Darting-Out Scenarios Near Parked Vehicles with Camera-Assisted Point Cloud Interpretation](https://mlsanity.com/paper/2508.04033/) (2025)

## Abstract (arXiv)

Radar sensors are an important part of driver assistance systems and intelligent vehicles due to their robustness against all kinds of adverse conditions, e.g., fog, snow, rain, or even direct sunlight. This robustness is achieved by a substantially larger wavelength compared to light-based sensors such as cameras or lidars. As a side effect, many surfaces act like mirrors at this wavelength, resulting in unwanted ghost detections. In this article, we present a novel approach to detect these ghost objects by applying data-driven machine learning algorithms. For this purpose, we use a large-scale automotive data set with annotated ghost objects. We show that we can use a state-of-the-art automotive radar classifier in order to detect ghost objects alongside real objects. Furthermore, we are able to reduce the amount of false positive detections caused by ghost images in some settings.