On observability and optimal gain design for distributed linear filtering and prediction

• URL: http://arxiv.org/abs/2203.03521v1
• Date: Mon, 7 Mar 2022 17:11:42 GMT
• Title: On observability and optimal gain design for distributed linear filtering and prediction
• Authors: Subhro Das
• Abstract summary: This paper presents a new approach to distributed linear filtering and prediction. Inspired by the consensus+innovations type of distributed estimation approaches, this paper proposes a novel algorithm that fuses the concepts of consensus and innovations.
• Score: 6.624726878647541
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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