摘要
本文提出了一种基于分步式滤波的多传感器动态系统数据融合算法 .在由多传感器组成的分布式动态系统中 ,当对目标状态的所有观测值到来时 ,首先基于系统先前信息对该时刻目标状态进行预测估计 ,利用Kalman滤波器和各局部观测值依次对该时刻目标状态的估计值进行更新 ,从而得到该时刻目标状态基于全局信息的融合估计值 .文中详细推证了融合算法的具体形式 ,并与传统的集中式数据融合算法在计算复杂度上进行了比较 ,计算机仿真表明该算法与传统的集中式算法对目标状态具有相同的估计精确度 .
This paper develops a data fusion algorithm of multisensor dynamic system based on filtering step by step.In distributed multisensor dynamic system when all of the observations aiming at the target are obtained,fistly we can predict the object state based on previous system information at this point and then use Kalman filtering and all of local observations to update the estimate value of object state in turn.Accordingly we can get a global fusion estimate value of object state based on the global information at that point.It presents the material form of this new algorithm and compares complexity of algorithm with traditional centralized data fusion algorithm.The computer simulation indicates that this algorithm possesses uniform estimate accuracy aiming at the object state with traditional centralized data fusion algorithm.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2004年第8期1264-1267,共4页
Acta Electronica Sinica
基金
国家自然科学基金 (No 60 1 740 1 1
60 3740 2 0 )
河南省杰出青年科学基金 (No 0 31 2 0 0 1 90 0 )
河南省高校杰出科研人才创新工程项目 (No.2 0 0 2KYCX0 0 7)
河南省国际合作项目 (No .0 4 4 6650 0 0 6)
