一种优化的贝叶斯估计多传感器数据融合方法

由于来自多个传感器的测量数据总是有一定程度的不确定性和不一致性,采用多传感器数据融合算法将多个节点的测量数据进行数据融合,利用数据的冗余度来减小这种不确定性,得到高可靠性的数据信息。提出了一种优化的贝叶斯估计多传感器数据融合方法,将贝叶斯估计和卡尔曼滤波器结合起来,应用于无线传感网络数据融合中。根据滤波器应用到传感数据、融合数据或者两者的方式,提出3种不同的技术,即:前向滤波法、后向滤波法和前后向滤波法。通过一个实例研究估计移动机器人的位置,验证算法的有效性。实验表明,在集中式和分布式两个方面数据融合体系结构,结合卡尔曼滤波器的贝叶斯融合算法能够有效地解决数据的不确定性和不一致性。

An Optimal Method of Data Fusion for Multi-sensors Based on Bayesian Estimation

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 in this paper, 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.