基于卡尔曼滤波的多传感器测量数据融合Multisensor measured data fusion based on Kalman filtering
李海艳;李维嘉;黄运保;
摘要(Abstract):
为解决最小二乘数据融合方法不能显式考虑测量的不确定性等问题,提出基于Kalman滤波的多传感器测量数据融合方法,此方法不仅显式考虑各测量设备的不确定性,而且还能实现单点和批量融合数据,有助于用户根据测量数据的多少选择有效的融合方法;且能有效地过滤基于Mahalanobis统计距离的异常噪声点.实例证明,此方法能获得高质量的融合曲面.
关键词(KeyWords): 卡尔曼滤波;多传感器测量;数据融合
基金项目(Foundation): 国家自然科学基金项目(编号:60804050)
作者(Authors): 李海艳;李维嘉;黄运保;
参考文献(References):
- [1]滕召胜,罗隆福.智能检测系统与数据融合[M].北京:机械工业出版,1999.
- [2]张明路,戈新良,唐智强,刘兴荣.多传感器信息融合技术研究现状和发展趋势[J].河北工业大学学报,2003,32(2):30-35.
- [3]Kalman R E.A new approach to linear filtering andprediction problems[J].Transactions of the ASMEJournal of Basic Engineering,1960,82(series D):35-45.
- [4]Hugh F,Durrant-Whyte.Consistent integration andpropagation of disparate sensor observations[J].TheInternational Journal of Robotics Research,1987,6(3):3-24.
- [5]Hugh F,Durrant-Whyte.Sensor models and multisen-sor integration[J].The International Journal of Ro-botics Research,1988,7(6):97-113.
- [6]Bogler P L.Shafer-dempster reasoning with applica-tion to multisensor target identification systems[J].IEEE Trans.on Systems,Man and Cybernetics,1987,17(6):968-977.
- [7]Hong L,Lynch A.Recursive temporal-spatial infor-mation fusion with application to target identification[J].IEEE Trans.on Aerospace and Electronic Sys-tems,1993,29(2):435-445.
- [8]何树权,钱健民.专家系统在数据融合技术中的应用研究[J].火控雷达技术,2003,32:67-76.
- [9]史忠科.最优估计的计算方法[M].北京:科学出版社,2001.
- [10]Rusinkiewics S,Levoy M.Efficient variants of theICP algorithm[C]//Proceedings of 3DDigital Imagingand Modeling,2001:145-152.
- [11]Pinheiro P,Lima P.Bayesian sensor fusion for coop-erative object localization and world modeling[C]//The 8th Conference on Intelligent Autonomous Sys-tems,Amsterdam,The Netherlands,2004.