AiDT: Toward Radar-Based Joint Anti-Interference Detection and Tracking for Weak Extended Targets Under Zero-Trust Autonomous Perception Tasks

Abstract

Extended object detection and tracking (EODT) is becoming a promising alternative for autonomous perception, which provides not only common motion states but also accurate spatial extent information, such as shape and size estimations. However, due to uncoordinated radar transmissions in zero-trust autonomous driving scenarios, radar-based EODT systems suffer from mutual radio frequency (RF) interference launched by attackers, leading to ghost targets and increased noise. On this account, a novel joint anti-interference detection and tracking system for weak extended targets is presented in this article. In contrast to pioneering works that treat object detection and tracking as two separate steps, the proposed method handles them jointly by integrating a continuous detection process into tracking, improving the detectability of weak targets. More specifically, to accommodate the time-varying number and extended size of radar reflections, an adaptive spatial distribution model representing the deformable extents is incorporated to capture the contour evolution over time. The key insight is that by accumulating the reflected power, all backscattered points are regarded as one entity to match the real target so that the intractable data association problem can be circumvented in the proposed method. Unlike the prominent random matrix model-based approaches that split motion and extent states into independent parts, this study explores the interdependencies between the states and updates them simultaneously. In addition, the proposed system has been deployed on a low-cost automotive radar platform. Experimental results confirm that the proposed approach can achieve accurate and resilient EODT against RF interference attacks, especially in occlusion, dynamic motion switching, and complex multiple extended target tracking scenarios.