传感技术学报Issue(5)：643-648,6.DOI:10.3969/j.issn.1004-1699.2014.05.014
一种优化的贝叶斯估计多传感器数据融合方法
An Optimal Method of Data Fusion for Multi-Sensors Based on Bayesian Estimation
摘要
Abstract
Data provided by sensors is always affected by some level of uncertainty in the measurements. Combining data from several sources using multi-sensor data fusion algorithms exploits the data redundancy to reduce this uncertainty and to achieve improved accuracy. An optimal method of data fusion for multi-sensor based on Bayesian Estimation is presented,which relies on combining a Bayesian fusion algorithm with Kalman filter in WSNs. Three different approaches namely:Pre-Filtering,Post-Filtering and Pre-Post-Filtering are described based on how filtering is applied to sensor data,to fused data or both. A case study of estimating the position of a mobile robot to verify if the proposed algorithm is valid is presented. Experimental study shows that combining Bayesian fusion algorithm with Kalman filter can help in handling the problem of uncertainty and inconsistency of the data in both centralized and decentralized data fusion architectures.关键词
无线传感器网络/数据融合/贝叶斯估计/卡尔曼滤波器Key words
wireless sensor network/data fusion/Bayesian estimation/Kalman filter分类
信息技术与安全科学引用本文复制引用
张品,董为浩,高大冬..一种优化的贝叶斯估计多传感器数据融合方法[J].传感技术学报,2014,(5):643-648,6.基金项目
国家自然科学基金项目(61271214) （61271214）
