Title
State fusion with unknown correlation: Ellipsoidal intersection
Author
Sijs, J.
Lazar, M.
van den Bosch, P.P.J.
TNO Industrie en Techniek
Publication year
2010
Abstract
Some crucial challenges of estimation over sensor networks are reaching consensus on the estimates of different systems in the network and separating the mutual information of two estimates from their exclusive information. Current fusion methods of two estimates tend to bypass the mutual information and directly optimize the fused estimate. Moreover, both the mean and covariance of the fused estimate are fully determined by optimizing the covariance only. In contrast to that, this paper proposes a novel fusion method in which the mutual information results in an additional estimate, which defines a mutual mean and covariance. Both variables are derived from the two initial estimates. The mutual covariance is used to optimize the fused covariance, while the mutual mean optimizes the fused mean. An example of decentralized state estimation, where the proposed fusion method is applied, shows a reduction in estimation error compared to the existing alternatives. © 2010 AACC.
Subject
Physics & Electronics
DSS - Distributed Sensor Systems
TS - Technical Sciences
Electronics
Decentralized state estimation
State fusion
Decentralized state estimation
Estimation errors
Fusion methods
Initial estimate
Mutual informations
State fusion
Optimization
State estimation
Estimation
To reference this document use:
http://resolver.tudelft.nl/uuid:a8b12fa6-8d4a-43cb-9a6d-967e91ba4bea
TNO identifier
425176
ISBN
9781424474264
Source
2010 American Control Conference, ACC 2010, 30 June 2010 through 2 July 2010, Baltimore, MD, USA. Conference code: 81791, 3992-3997
Article number
No.: 5531237
Document type
conference paper