KAN-powered large-target detection for automotive radar

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.

Beamspace-based Sensing in Dynamic Joint Communication and Sensing Systems

Dual-function radar communication (DFRC) systems incorporate both radar and communication functions by sharing spectrum, hardware and RF signal processing chains. Future technologies, such as 6G, are envisioned to support multiple communication platforms along with radar sensing, thus leading to high dynamism and competition for the available resources. In such settings, whenever communication takes precedence, a likely scenario is dynamically changing RF chain and antenna availability for sensing. This necessitates real-time beam redesign to cover the field-of-view (FOV), solving which is intractable via computationally expensive optimization approaches. We propose that classic windowing techniques are still relevant and much more practical than optimization methods in such dynamic scenarios. Specifically, parametrized windows can be used in a strategic way to adapt to varying resource availability while sustaining sensing performance.