AI 中文总结
针对低空无人机跟踪面临的挑战，提出目标挂载IRS辅助的感知与通信一体化框架。建立无人机三维状态模型，用扩展卡尔曼滤波器跟踪，推导性能界，开发低复杂度联合波束成形设计，模拟显示该框架在复杂轨迹上精度高、功耗低、延迟小。
AI 中文摘要
本文提出了一种用于实时无人机跟踪的目标挂载智能反射面（IRS）辅助的感知与通信一体化框架，以应对低空环境下链路阻塞和雷达散射截面弱等挑战。通过将IRS集成到无人机上，系统创建了一个移动协作目标，既为自跟踪提供可控的视距回波，又作为移动中继增强地面通信。建立了机动无人机的三维状态演化模型，基于此模型实现扩展卡尔曼滤波器实时跟踪移动无人机。推导了后验克拉美罗界和椭圆权衡性能界的闭式表达式以量化感知精度与通信吞吐量的关系。开发了低复杂度联合波束成形设计，该方案能有效绕过传统方法耗时的数值迭代。数值模拟表明，该框架在复杂机动轨迹上显著优于传统固定部署基准，实现厘米级精度，同时大幅降低发射功率和处理延迟。
英文摘要
This paper proposes a target-mounted intelligent reflecting surface (IRS)-assisted integrated sensing and communication framework for real-time unmanned aerial vehicle (UAV) tracking, addressing challenges such as link blockage and weak radar cross section in the low-altitude economy. By integrating the IRS onto the UAV, the system creates a mobile cooperative target that provides controllable line-of-sight echoes for self-tracking while acting as a mobile relay for ground communication enhancement. We establish a comprehensive three dimensions state evolution model for the maneuvering UAV. Based on this model, an extended Kalman filter is immediately implemented to achieve real time tracking of the moving UAV. To characterize the fundamental theoretical limits of this recursive estimation process, we derive the analytical posterior Cramer Rao bound and a closed form expression for the elliptical tradeoff performance bound to quantify the relationship between sensing precision and communication throughput. To ensure millisecond level responsiveness, we develop a low complexity joint beamforming design. By utilizing the analytical mapping between tracking and communication requirements, the proposed scheme yields closed form solutions for beamforming vectors, effectively bypassing the time consuming numerical iterations of conventional methods. Numerical simulations demonstrate that the proposed framework significantly outperforms traditional fixed-deployment benchmarks across complex maneuvering trajectories, achieving centimeter-level accuracy while substantially reducing transmit power and processing latency.
