AI 中文总结
针对无人机海洋监测中的高海拔平台辅助稀疏协作ISAC问题，提出一种结构化可行关联图MARL框架，通过队列加权缓冲收集MDP和掩码顺序b匹配策略，实现感知、通信与移动性的联合优化，在拥挤海事场景中累计队列加权收集效用提升约106%。
AI 中文摘要
本文研究了高海拔平台（HAP）辅助的稀疏协作集成感知与通信（ISAC）用于无人机海洋监测。一组旋翼无人机感知漂流浮标，收集其监测数据，并将局部后验估计报告给执行融合和稀疏协作控制的HAP。模型明确考虑了空间相关的海斑场、斑块感知浮标动力学、RCS和杂波感知的回波感知、融合后验克拉美-罗下界（PCRB）以及推进能量受限的无人机移动性。长期目标被建模为队列加权缓冲收集马尔可夫决策过程而非瞬时吞吐量，其中每个浮标维护一个缓冲观测的积压。由此产生的长期设计被表述为一个混合离散连续问题，包含感知、通信、移动性、安全、缓冲收集和机载能量约束。为了在不使用确定性优化器替代学习的情况下解决组合关联部分，我们提出了一种结构化可行关联图-MARL框架。异构图编码器生成候选边logits，掩码顺序b匹配策略采样合法的无人机-浮标关联，同时精确满足无人机负载和浮标簇约束。然后指定了MAPPO风格的训练程序、独立队列状态值评论家和一致性验证协议，以支持可重复训练。在拥挤海事场景上的仿真结果表明，所提策略相比速率驱动的确定性解码器将累计队列加权收集效用提高了约106%，在海况扫描和中高流量负载下保持较大裕度，并可迁移到更大网络而无需微调。
英文摘要
This paper studies high-altitude platform (HAP)-assisted sparse cooperative integrated sensing and communication (ISAC) for UAV-enabled ocean monitoring. A fleet of rotary-wing UAVs senses drifting buoys, collects their monitoring data, and reports local posterior estimates to a HAP that performs fusion and sparse cooperation control. The model explicitly accounts for a spatially correlated sea-patch field, patch-aware buoy dynamics, RCS- and clutter-aware echo sensing, fused posterior Cramér-Rao bounds (PCRBs), and propulsion-energy-limited UAV mobility. The long-horizon objective is cast as a queue-weighted buffered-collection Markov decision process rather than instantaneous throughput, where each buoy maintains a backlog of buffered observations. The resulting long-horizon design is formulated as a mixed discrete-continuous problem with sensing, communication, mobility, safety, buffered-collection, and onboard-energy constraints. To address the combinatorial association component without replacing learning by a deterministic optimizer, we propose a structured feasible-association graph-MARL framework. A heterogeneous graph encoder produces candidate-edge logits, and a masked sequential b-matching policy samples legal UAV-buoy associations while exactly satisfying UAV-load and buoy-cluster constraints. A MAPPO-style training procedure, an independent queue-state value critic, and a consistency-verification protocol are then specified to support reproducible training. Simulation results on congested maritime scenarios show that the proposed policy improves the cumulative queue-weighted collection utility by about 106\% over the rate-driven deterministic decoder, maintains a large margin across sea-state sweeps and medium-to-heavy traffic loads, and transfers to larger networks without fine-tuning.
