Distributed Information Fusion Kalman Predictor for Stochastic Systems with Uncertain Observations

摘要：

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.

关键词： Uncertain observation Distributed weighted fusion Cross-covariance matriz Kalman predictor

作者: Zhang Teng Sun Shuli

作者单位: Department of Automation, School of Electronics Engineering, Heilongjiang University, Harbin 150080

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 1160-1163

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）