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DISTRIBUTED OPTIMAL CONSENSUS-BASED KALMAN FILTERING AND ITS RELATION TO MAP ESTIMATION

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Citation Author(s):
Shengdi Wang, Henning Paul, Armin Dekorsy
Submitted by:
Shengdi Wang
Last updated:
13 April 2018 - 9:35am
Document Type:
Presentation Slides
Document Year:
2018
Event:
Presenters Name:
Shengdi Wang
Paper Code:
SPCOM-L4.6

Abstract 

Abstract: 

In this paper, we address the problem of distributed state estimation, where a set of nodes are required to jointly estimate the state of a linear dynamic system based on sequential measurements. In our distributed scenario, all the nodes 1) are interested in the full state of the observed system and 2) pursue a consensus-based state estimate with high accuracy. We exploit the equivalent relation between the maximum-a-posteriori (MAP) estimation and the Kalman filter (KF) in the minimum mean square error (MMSE) sense under the Gaussian assumption. Utilizing this relation, a distributed Kalman filtering algorithm is derived, which ensures consensus-based state estimates among nodes and converges to the optimal central KF solution.

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