# KAN-powered large-target detection for automotive radar

arXiv: https://arxiv.org/abs/2502.19000 · HTML: https://mlsanity.com/paper/2502.19000/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2025.

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

- [Von Mises Based Uncertainty Quantification for Closely Spaced Automotive Radar Targets](https://mlsanity.com/paper/2606.31473/) (2026)

## Abstract (arXiv)

This paper presents a novel radar signal detection pipeline focused on detecting large targets such as cars and SUVs. Traditional methods, such as Ordered-Statistic Constant False Alarm Rate (OS-CFAR), commonly used in automotive radar, are designed for point or isotropic target models. These may not adequately capture the Range-Doppler (RD) scattering patterns of larger targets, especially in high-resolution radar systems. Additional modules such as association and tracking are necessary to refine and consolidate the detections over multiple dwells. To address these limitations, we propose a detection technique based on the probability density function (pdf) of RD segments, leveraging the Kolmogorov-Arnold neural network (KAN) to learn the data and generate interpretable symbolic expressions for binary hypotheses. Beside the Monte-Carlo study showing better performance for the proposed KAN expression over OS-CFAR, it is shown to exhibit a probability of detection (PD) of 96% when transfer learned with field data. The false alarm rate (PFA) is comparable with OS-CFAR designed with PFA = 10-6. Additionally, the study also examines impact of the number of pdf bins representing RD segment on performance of the KAN-based detection.