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：集成感知与通信（ISAC）借助无人机（UAV）实现，是促进目标跟踪应用的一项有前景的技术。与主要关注估计目标位置的常规基于UAV的ISAC系统设计不同，目标速度估计因其对链路维持和实时响应的关键影响也需加以考虑，这要求对资源分配和跟踪方案进行新的设计。本文针对无人机赋能ISAC系统提出了一种基于扩展卡尔曼滤波的跟踪方案，其中无人机跟踪移动物体并与附着在该物体上的设备进行通信。具体而言，通过优化无人机轨迹，最小化物体相对位置和速度估计的预测后验克拉美-罗界（PCRB）的加权和，并基于逐次凸逼近方法获得高效解。此外，在测量均方误差（MSE）的特殊情况下，获得最优相对运动状态并证明其保持固定仰角和零相对速度。数值结果验证了当预测测量MSE主导预测PCRB时，所获得的预测PCRB最小化解可通过最优相对运动状态近似，同时证实了所提跟踪方案的有效性。此外，还阐明了由固定仰角导致的系统性能上的三种有趣权衡关系。