Integrated sensing and communications (ISAC) enabled by unmanned aerial vehicles (UAVs) is a promising technology to facilitate target tracking applications. In contrast to conventional UAV-based ISAC system designs that mainly focus on estimating the target position, the target velocity estimation also needs to be considered due to its crucial impacts on link maintenance and real-time response, which requires new designs on resource allocation and tracking scheme. In this paper, we propose an extended Kalman filtering-based tracking scheme for a UAV-enabled ISAC system where a UAV tracks a moving object and also communicates with a device attached to the object. Specifically, a weighted sum of predicted posterior Cram\'er-Rao bound (PCRB) for object relative position and velocity estimation is minimized by optimizing the UAV trajectory, where an efficient solution is obtained based on the successive convex approximation method. Furthermore, under a special case with the measurement mean square error (MSE), the optimal relative motion state is obtained and proved to keep a fixed elevation angle and zero relative velocity. Numerical results validate that the obtained solution to the predicted PCRB minimization can be approximated by the optimal relative motion state when predicted measurement MSE dominates the predicted PCRBs, as well as the effectiveness of the proposed tracking scheme. Moreover, three interesting trade-offs on system performance resulted from the fixed elevation angle are illustrated.

翻译：无人机（UAV）赋能的集成感知与通信（ISAC）技术是推动目标跟踪应用的关键技术。与主要聚焦于目标位置估计的传统无人机ISAC系统设计不同，目标速度估计因对链路维持和实时响应的关键影响也必须纳入考量，这需要在资源分配与跟踪方案上进行全新设计。本文针对无人机赋能ISAC系统（其中无人机跟踪移动目标并通过附属于目标的设备进行通信），提出了一种基于扩展卡尔曼滤波的跟踪方案。具体而言，通过优化无人机轨迹，最小化目标相对位置与速度估计的加权预测后验克拉美-罗界（PCRB），并基于逐次凸近似方法获得高效解。此外，在量测均方误差（MSE）的特殊情况下，推导了最优相对运动状态，证明其需保持固定俯仰角与零相对速度。数值结果表明，当预测量测MSE主导预测PCRB时，通过最小化预测PCRB获得的解可近似为最优相对运动状态，同时验证了所提跟踪方案的有效性。最后，揭示了由固定俯仰角引起的三种系统性能权衡关系。