Decentralized data fusion with inverse covariance intersection

Decentralized data fusion with inverse covariance intersection

Noack, B.
Sijs, J.
Reinhardt, M.
Hanebeck, U.D.

2017

In distributed and decentralized state estimation systems, fusion methods are employed to systematically combine multiple estimates of the state into a single, more accurate estimate. An often encountered problem in the fusion process relates to unknown common information that is shared by the estimates to be fused and is responsible for correlations. If the correlation structure is unknown to the fusion method, conservative strategies are typically pursued. As such, the parameterization introduced by the ellipsoidal intersection method has been a novel approach to describe unknown correlations, though suitable values for these parameters with proven consistency have not been identified yet. In this article, an extension of ellipsoidal intersection is proposed that guarantees consistent fusion results in the presence of unknown common information. The bound used by the novel approach corresponds to computing an outer ellipsoidal bound on the intersection of inverse covariance ellipsoids. As a major advantage of this inverse covariance intersection method, fusion results prove to be more accurate than those provided by the well-known covariance intersection method. © 2017 Elsevier Ltd

2015 Observation, Weapon & Protection Systems
AS - Acoustics & Sonar
TS - Technical Sciences
Covariance intersection
Data fusion
Decentralized Kalman filtering
Sensor fusion
State estimation
Data fusion
Inverse problems
State estimation
Correlation structure
Covariance intersection
Decentralized data fusion
Decentralized state estimation
Ellipsoidal intersections
Inverse covariance
Kalman-filtering
Sensor fusion
Sensor data fusion

http://resolver.tudelft.nl/uuid:cfeeca99-6dff-486a-a6a2-2c69e6bfa8b4

749112

Elsevier Ltd

0005-1098

Automatica, 79, 35-41

article