论文标题
关于分布式线性过滤和预测的可观察性和最佳增益设计
On observability and optimal gain design for distributed linear filtering and prediction
论文作者
论文摘要
本文提出了一种新的方法来分布线性过滤和预测。所考虑的问题由一个由网络稀疏的传感器网络观察到的随机动力系统组成。受分布式估计方法的共识+创新类型的启发，本文提出了一种融合共识和创新概念的新型算法。本文介绍了提出的算法所要求的分布式可观察性的定义，这是一个比全球可观察性和连接的网络假设合并在一起的假设弱的。遵循第一原则，设计了最佳增益矩阵，以使每个代理的均方估计误差被最小化，并且代数riccati方程的分布式版本被得出用于计算增益。
This paper presents a new approach to distributed linear filtering and prediction. The problem under consideration consists of a random dynamical system observed by a multi-agent network of sensors where the network is sparse. Inspired by the consensus+innovations type of distributed estimation approaches, this paper proposes a novel algorithm that fuses the concepts of consensus and innovations. The paper introduces a definition of distributed observability, required by the proposed algorithm, which is a weaker assumption than that of global observability and connected network assumptions combined together. Following first principles, the optimal gain matrices are designed such that the mean-squared error of estimation is minimized at each agent and the distributed version of the algebraic Riccati equation is derived for computing the gains.
