Unmanned aerial vehicles (UAVs) are increasingly deployed in mission-critical applications such as target tracking, where they must simultaneously sense dynamic environments, ensure reliable communication, and achieve precise control. A key challenge here is to jointly guarantee tracking accuracy, communication reliability, and control stability within a unified framework. To address this issue, we propose an integrated sensing, communication, and control (ISCC) framework for UAV-assisted target tracking, where the considered tracking system is modeled as a discrete-time linear control process, with the objective of driving the deviation between the UAV and target states toward zero. We formulate a stochastic model predictive control (MPC) optimization problem for joint control and beamforming design, which is highly non-convex and intractable in its original form. To overcome this difficulty, the target state is first estimated using an extended Kalman filter (EKF). Then, by deriving the closed-form optimal beamforming solution under a given control input, the original problem is equivalently reformulated into a tractable control-oriented form. Finally, we convexify the remaining non-convex constraints via a relaxation-based convex approximation, yielding a computationally tractable convex optimization problem that admits efficient global solution. Numerical results show that the proposed ISCC framework achieves tracking accuracy comparable to a non-causal benchmark while maintaining stable communication, and it significantly outperforms the conventional control and tracking method.

翻译：无人机在目标跟踪等关键任务应用中日益普及，其需同时感知动态环境、确保可靠通信并实现精确控制。核心挑战在于如何在统一框架内联合保障跟踪精度、通信可靠性与控制稳定性。为此，本文提出一种面向无人机辅助目标跟踪的集成感知、通信与控制框架，其中跟踪系统被建模为离散时间线性控制过程，目标在于驱动无人机与目标状态间的偏差趋近于零。我们构建了一个用于联合控制与波束成形设计的随机模型预测控制优化问题，该问题在原始形式下具有高度非凸性与求解难度。为克服此困难，首先采用扩展卡尔曼滤波器对目标状态进行估计；随后，通过推导给定控制输入下的闭式最优波束成形解，将原问题等价转化为可处理的面向控制的形式；最后，通过基于松弛的凸近似方法对剩余非凸约束进行凸化处理，从而得到可计算求解的凸优化问题，该问题存在高效的全局解。数值结果表明，所提出的ISCC框架在保持稳定通信的同时，其跟踪精度可与非因果基准方法相媲美，且显著优于传统控制与跟踪方法。