On observability and optimal gain design for distributed linear filtering and prediction
Abstract
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.
Cite
@article{arxiv.2203.03521,
title = {On observability and optimal gain design for distributed linear filtering and prediction},
author = {Subhro Das},
journal= {arXiv preprint arXiv:2203.03521},
year = {2022}
}
Comments
8 pages