IOS Aided Extended Target Tracking in ISAC Networks: A Zeroth-Order Approach

Integrated Sensing and Communication (ISAC) technology facilitates simultaneous reliable communication and high-precision sensing performance in vehicular networks. Many existing ISAC models treat vehicles as point-like objects, which oversimplifies real-world scenarios. In practice, vehicles have complex shapes and sizes, which may occupy multiple range and angle grids. To address these challenges, we propose an intelligent omni-surface (IOS) mounted on the top surface of an extended vehicle and introduce a novel IOS-aided extended vehicle tracking scheme. Aiming to minimize the Cramér-Rao bound (CRB) for estimating vehicle's angle, distance and velocity while meeting communication rate requirements, we propose a zeroth-order optimization based increasing penalty dual decomposition (ZO-IPDD) algorithm. Additionally, a dimension reduction strategy is employed to mitigate the high computational complexity. Numerical results demonstrate the superiority of the proposed algorithm and scheme.