On observability and optimal gain design for distributed linear filtering and prediction
This work addresses distributed estimation in sensor networks, offering a method with reduced assumptions, but it appears incremental as it builds on consensus+innovations approaches without broad SOTA claims.
The paper tackles distributed linear filtering and prediction in sparse multi-agent sensor networks by proposing a novel algorithm that fuses consensus and innovations, requiring a weaker distributed observability assumption than global observability and connected networks combined, and designs optimal gain matrices to minimize mean-squared error at each agent, deriving a distributed algebraic Riccati equation for gain computation.
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.