标题： 具有动态拓扑和异构传感器网络的多智能体系统的分散输入和状态估计 显示英文标题

标题： Decentralized Input and State Estimation for Multi-agent System with Dynamic Topology and Heterogeneous Sensor Network

摘要： 去中心化系统中的一个重要挑战是在存在未知输入的情况下进行状态估计，特别是在具有动态拓扑的异构传感器网络中。 尽管已经引入了许多共识算法，但它们通常需要大量的信息交换或多次通信迭代来确保估计的准确性。 本文提出了一种高效的算法，能够实现与完全了解其他代理信息的滤波器相当的无偏且最优的解决方案。 这是通过使用信息滤波器分解和通过协方差交集融合输入来实现的。我们的方法只需要一次通信迭代即可在代理之间交换各自的估计值，而不是多次的信息交换，从而通过避免共享显式观测值和系统方程来保护代理的隐私。 此外，为了应对动态通信拓扑带来的挑战，我们提出了两种实用策略来处理间歇性观测和不完整状态估计所引起的问题，从而提高了估计过程的鲁棒性和准确性。 在静态和动态环境下的实验和消融研究证明了我们算法相对于其他基线算法的优越性。值得注意的是，它表现得与那些拥有全局邻居视图的算法一样好，甚至更好。 显示英文摘要

摘要： A crucial challenge in decentralized systems is state estimation in the presence of unknown inputs, particularly within heterogeneous sensor networks with dynamic topologies. While numerous consensus algorithms have been introduced, they often require extensive information exchange or multiple communication iterations to ensure estimation accuracy. This paper proposes an efficient algorithm that achieves an unbiased and optimal solution comparable to filters with full information about other agents. This is accomplished through the use of information filter decomposition and the fusion of inputs via covariance intersection. Our method requires only a single communication iteration for exchanging individual estimates between agents, instead of multiple rounds of information exchange, thus preserving agents' privacy by avoiding the sharing of explicit observations and system equations. Furthermore, to address the challenges posed by dynamic communication topologies, we propose two practical strategies to handle issues arising from intermittent observations and incomplete state estimation, thereby enhancing the robustness and accuracy of the estimation process. Experiments and ablation studies conducted in both stationary and dynamic environments demonstrate the superiority of our algorithm over other baselines. Notably, it performs as well as, or even better than, algorithms that have a global view of all neighbors.