We describe a consensus-based distributed filtering algorithm for linear systems with a parametrized gain and show that when the parameter becomes large the error covariance at each node becomes arbitrarily close to the error covariance of the optimal centralized Kalman filter. The result concerns distributed estimation over a connected un-directed or directed graph and for static configurations it only requires to exchange the estimates among adjacent nodes. A comparison with related approaches confirms the theoretical results and shows that the method can be applied to a wide range of distributed estimation problems.

中文翻译：

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连续时间线性系统基于渐近最优共识的分布式滤波

我们描述了一种具有参数化增益的线性系统的基于共识的分布式滤波算法，并表明当参数变大时，每个节点处的误差协方差将任意接近最佳集中式卡尔曼滤波器的误差协方差。结果涉及在连接的无向图或有向图上的分布式估计，对于静态配置，只需要在相邻节点之间交换估计即可。与相关方法的比较证实了理论结果，并表明该方法可以应用于广泛的分布式估计问题。