Convergence and Accuracy Analysis for A Distributed Static State Estimator based on Gaussian Belief Propagation

2020/04/04 by Damián Marelli, Tianju Sui, Marelli, Damián +6 · 1 citation
Computer Science · Engineering · #Distributed Sensor Networks and Detection Algorithms #Target Tracking and Data Fusion in Sensor Networks #Underwater Vehicles and Communication Systems #cs.SY #eess.SY

paper · pdf · doi:10.48550/arxiv.2004.01969

arxiv created 2020/04/04 · arxiv updated 2020/04/07

Abstract

This paper focuses on the distributed static estimation problem and a Belief Propagation (BP) based estimation algorithm is proposed. We provide a complete analysis for convergence and accuracy of it. More precisely, we offer conditions under which the proposed distributed estimator is guaranteed to converge and we give concrete characterizations of its accuracy. Our results not only give a new algorithm with good performance but also provide a useful analysis framework to learn the properties of a distributed algorithm. It yields better theoretical understanding of the static distributed state estimator and may generate more applications in the future.

Citations

• Consensus Problems in Networks of Agents With Switching Topology and Time-Delays

Cited by

• Fundamental Linear Algebra Problem of Gaussian Inference

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