基于Kalman滤波的多传感器分布式数据融合算法及其并行实现(英文)

• 作 者: 郭强 郁松年
• 机 构: School of Computer Engineering and Science Shanghai University Shanghai 200072 P.R. China,School of
• 出 处: Journal of Shanghai University
• 发表日期: 2006年02期
• 关键词: 数据融合算法 传感器 英文 centralized independence subtraction plication acceleration incomp

The purpose of data fusion is to produce an improved model or estimate of a system from a set of independent data sources. Various multisensor data fusion approaches exist, in which Kalman filtering is important. In this paper, a fusion algorithm based on multisensor systems is discussed and a distributed multisensor data fusion algorithm based on Kalman filtering presented. The algorithm has been implemented on cluster-based high performance computers. Experimental results show that the method produces precise estimation in considerably reduced execution time.

The purpose of data fusion is to produce an improved model or estimate of a system from a set of independent data sources. Various this paper fusion algorithm based on multisensor systems is discussed and a distributed multisensor data fusion algorithm based on Kalman filtering presented. The algorithm has been implemented on cluster-based high performance computers. Experimental results show that the method produces precise estimation in significantly reduced execution time.