Distributed information fusion Kalman predictor for stochastic systems with uncertain observations

Authors:
Zhang Teng;Sun Shuli
Affiliations:
Department of Automation, School of Electronics Engineering, Heilongjiang University, Harbin;Department of Automation, School of Electronics Engineering, Heilongjiang University, Harbin
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Year:
2009

Citing 3
Cited 0

Recursive estimator for linear and nonlinear systems with uncertain observations

Signal Processing
Multi-sensor optimal information fusion Kalman filter

Automatica (Journal of IFAC)
Optimal linear estimation fusion .I. Unified fusion rules

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

In sensor networks, sensor measurements may be uncertain due to the impact of environment and different performances of sensors. In this paper, the cross-covariance matrix of prediction errors between any two sensor subsystems is derived for stochastic discrete-time linear systems with uncertain observations by using projection theory. Based on the linear minimum variance weighted fusion algorithm, the distributed information fusion Kalman predictor is obtained for stochastic systems with uncertain observations. It avoids the high-dimensional computation resorting to state augmentation, and has the better reliability. The simulation example verifies the effectiveness of the algorithm.