异常情况下基于贝叶斯的多传感器融合方法

Multi-sensor fusion method based on Bayesian in singular conditions

针对多传感器融合过程中异常测量数据的出现会降低数据融合质量的问题,提出了基于贝叶斯方法的一种多传感器数据融合方法,通过识别传感器间测量数据的不一致,在传感器数据融合前剔除异常数据,提高数据融合的精度。提出的方法在简单的贝叶斯方法上增加一项概率因子,以此表征测量数据为非异常事件的概率。在某只传感器输出数据与其他传感器不一致时,增加的因子项具有增加后验分布方差的效果。通过仿真实验对融合方法进行了验证,结果表明该方法能有效识别传感器数据间的不一致,融合精度得到一定提高。

The occurrence of spurious data will degrade the quality of data fusion in the process of multi-sensor fusion. This paper presents a sensor data fusion method based on Bayesian method that can identify the inconsistency in sensor data so that spurious data can be eliminated from the sensor fusion process.The proposed method adds a term to the commonly used Bayesian technique that represents the probabilistic estimate corresponding to the event that the data is not spurious conditioned upon the data and the true state.This term has the effect of increasing the variance of the posterior distribution when data from one of the sensors is inconsistent with respect to the other.The proposed strategy is verified with the help of extensive simulations.The simulations show that the proposed method is able to identify inconsistency in sensor data and also confirmed that the effective identification of inconsistency led to a better fusion accuracy.