KAN-powered large-target detection for automotive radar

TL;DR

This paper introduces a KAN-powered detection method for large automotive targets that outperforms traditional OS-CFAR, achieving high detection probability with low false alarms in high-resolution radar systems.

Contribution

The paper proposes a novel KAN-based detection technique leveraging RD segment PDFs, improving large target detection in automotive radar over traditional methods.

Findings

KAN-based detection achieves 96% probability of detection.

False alarm rate is comparable to OS-CFAR at PFA = 10^-6.

Performance depends on the number of PDF bins used.

Abstract

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.