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.

翻译：去中心化系统中的一个关键挑战在于存在未知输入情况下的状态估计，特别是在具有动态拓扑的异构传感器网络中。尽管已有众多共识算法被提出，但它们通常需要大量的信息交换或多轮通信迭代来确保估计精度。本文提出一种高效算法，能够实现与完全掌握其他智能体信息的滤波器相媲美的无偏最优解。该算法通过信息滤波器分解和基于协方差交叉的输入融合来实现。我们的方法仅需在智能体之间进行单次通信迭代以交换个体估计值，而非多轮信息交换，从而通过避免共享显式观测值和系统方程来保护智能体的隐私。此外，为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发的问题，从而提升了估计过程的鲁棒性和准确性。在静态和动态环境中进行的实验与消融研究证明了我们的算法相较于其他基线方法的优越性。值得注意的是，其性能与那些具备全局邻居视图的算法相当甚至更优。