面向任务关键型ISAC：无人机群中可靠的能量感知协调
Toward Mission-Critical ISAC: Reliable Energy-Aware Coordination in UAV Swarms
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中文总结 AI 辅助
针对任务关键型无人机群，提出两层架构与后退时域交替优化算法，联合优化轨迹、时间分配与无线充电，显著降低感知误差并保障能量可持续性。
中文摘要 AI 辅助
在任务关键型应用中部署的无人机群必须同时跟踪移动空中目标并维持可靠的数据链路。然而，主动式集成感知与通信（ISAC）操作对推进和传输施加了双重能量负担，通过过早的电池耗尽威胁任务连续性。本文提出了一种两层无人机群架构，其中任务无人机（MUAV）执行协作式ISAC以跟踪移动空中目标，而配备太阳能收集板的专用充电无人机（CUAV）通过空中无人机间无线电力传输（WPT）为低电量MUAV补充能量。我们制定了联合最小化问题，涉及MUAV轨迹上的协作后验克拉美-罗下界（PCRB）、每时隙感知-通信时间分配、WPT调度和准入，以及CUAV会合轨迹，同时受最小上行速率、双层能量因果性、WPT邻近性、碰撞避免和速度约束，产生一个非凸混合整数规划（MIP），据我们所知，这是首个将协作ISAC感知质量与空中WPT和双层能量管理联合耦合的模型。为高效求解，我们提出了后退时域交替优化（RHAO），一种四块每时隙算法，将问题分解为：通过匈牙利算法进行充电准入，通过连续凸近似（SCA）进行MUAV轨迹和时间分配，CUAV会合，以及WPT功率分配，并具有单调收敛保证。仿真结果表明，与固定时间分配基线相比，RHAO将平均PCRB降低了16.8倍，与静态轨迹方案相比降低了5.4倍，而空中WPT子系统在整个任务期间将所有MUAV维持在能量临界阈值以上。
英文摘要
Unmanned aerial vehicle (UAV) swarms deployed in mission-critical applications must simultaneously track a mobile aerial target and maintain reliable data links. However, active integrated sensing and communication (ISAC) operation imposes a dual energy burden on propulsion and transmission, threatening mission continuity through premature battery depletion. In this paper, we propose a two-tier UAV swarm architecture in which mission UAVs (MUAVs) execute cooperative ISAC for mobile aerial target tracking while dedicated charging UAVs (CUAVs), equipped with solar harvesting panels, replenish low-battery MUAVs via aerial UAV-to-UAV wireless power transfer (WPT). We formulate the joint minimization of the cooperative posterior Cramér-Rao bound (PCRB) over MUAV trajectories, per-slot sensing-communication time splits, WPT scheduling and admission, and CUAV rendezvous trajectories, subject to minimum uplink rate, dual-tier energy causality, WPT proximity, collision-avoidance, and speed constraints, yielding a non-convex mixed-integer program (MIP) that, to the best of our knowledge, is the first to jointly couple cooperative ISAC sensing quality with aerial WPT and dual-tier energy management. To solve it efficiently, we propose Receding-Horizon Alternating Optimization (RHAO), a four-block per-slot algorithm that decomposes the problem into: charging admission via the Hungarian algorithm, MUAV trajectory and time-split via successive convex approximation (SCA), CUAV rendezvous, and WPT power allocation, with monotone convergence guarantees. Simulation results demonstrate that RHAO reduces the mean PCRB by 16.8 times over a fixed-time-split baseline and 5.4 times over a static-trajectory scheme, while the aerial WPT subsystem sustains all MUAVs above the energy-critical threshold throughout the full mission horizon.
发表机构
- ICTFICIAL Oy（ICTFICIAL Oy公司）
- Ruhr University Bochum（波鸿鲁尔大学）
机构由 AI 辅助整理，请以论文原文为准。
