基于匹配理论的无人机辅助远程状态估计与资源分配研究

       信息物理系统（Cyber-physical Systems, CPS）利用智能传感器网络以及自动闭环的数据流将物理世界和网络空间集成为一个统一的实体。通过无线通信技术，CPS可以实现远程监控和远程控制。由于无人机（Unmanned Aerial Vehicle, UAV）具有良好传输视距、高移动性和快速计算等优点，因此，可以利用无人机作为通信中继并进行本地计算，从而实现有效地辅助远程状态估计。

       本文研究了基于CPS的无人机辅助远程状态估计问题。首先，本文提出了新颖的无人机辅助状态估计系统框架，该框架充分利用了无人机具有良好的通信视距以及具有灵活的计算能力等优点，实现了对系统状态估计误差协方差的最小化。其次，针对基于CPS的无人机辅助状态估计问题，本文考虑联合优化无人机资源分配与无人机路径规划，从而实现系统状态估计误差最小化；本文将复杂的最优远程状态估计问题解耦为两个子问题，分别是无人机与传感器之间的资源分配问题和无人机路径规划问题。然后，针对无人机资源分配问题，本文采用基于匹配理论的动态延迟接受算法得到无人机与传感器之间的稳定匹配；针对无人机路径规划问题，本文采用了动态规划算法获得无人机的最佳飞行路径；通过迭代优化两个子问题，得到问题的次优解。最后，理论分析证明了匹配算法得到的匹配是稳定的，并证明了无人机最优路径存在周期性；仿真分析验证了无人机最优路径的周期性以及所提出方案的可行性和有效性。

       本文针对无人机与传感器之间存在交易的资源分配问题，提出了基于匹配理论的差分隐私信道资源双向拍卖机制。不同于大多数拍卖机制，本文提出的拍卖机制充分考虑了拍卖参与者的偏好以及隐私保护。首先，本文针对基于隐私保护的拍卖机制，引入差分隐私来保护无人机与传感器拍卖过程中的出价和投标价格，从而保证拍卖的公平进行。其次，通过基于匹配理论的延迟接受算法将在拍卖中胜出的无人机与传感器根据它们的偏好进行匹配。然后，通过理论分析，本文证明了本文提出的算法得到的匹配结果是稳定的，并且实现了 $\epsilon$-差分隐私；本文证明了所提出的拍卖机制确保了买方和卖方均具有真实性并且实现了社会福利局部最大化。最后，仿真分析结果表明，本文提出的算法具有较好的经济收益和隐私保护效果。

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