In this paper, we investigate a joint computation offloading and target tracking in Integrated Sensing and Communication (ISAC)-enabled unmanned aerial vehicle (UAV) network. Therein, the UAV has a computing task that is partially offloaded to the ground UE for execution. Meanwhile, the UAV uses the offloading bit sequence to estimate the velocity of a ground target based on an autocorrelation function. The performance of the velocity estimation that is represented by Cramer-Rao lower bound (CRB) depends on the length of the offloading bit sequence and the UAV's location. Thus, we jointly optimize the task size for offloading and the UAV's location to minimize the overall computation latency and the CRB of the mean square error for velocity estimation subject to the UAV's budget. The problem is non-convex, and we propose a genetic algorithm to solve it. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm.

翻译：本文研究了集成感知与通信（ISAC）赋能的无人机（UAV）网络中的联合计算卸载与目标跟踪问题。其中，无人机将部分计算任务卸载至地面用户设备执行，同时利用卸载比特序列基于自相关函数估计地面目标的速度。基于克拉美-罗下界（CRB）表示的速度估计性能取决于卸载比特序列长度与无人机位置。因此，我们联合优化卸载任务规模与无人机位置，以在无人机预算约束下最小化整体计算延迟与速度估计均方误差的CRB。该问题为非凸优化，我们提出一种遗传算法进行求解。仿真结果验证了所提算法的有效性。