传感技术学报2018，Vol.31Issue(6)：915-919,5.DOI:10.3969/j.issn.1004-1699.2018.06.017
用于胎压监测系统的一种改进贝叶斯估计数据融合的研究
Research on an Improved Bayesian Estimation Data Fusion for Tire Pressure Monitoring System
安世奇 1由东媛1
作者信息
- 1. 青岛科技大学自动化与电子工程学院,山东 青岛266000
- 折叠
摘要
Abstract
Measurement data by single sensor is full of uncertainty in small car tire pressure monitoring system ( TPMS) . To solve this problem,combining Bayesian estimation with Kalman filter based on multi-sensor data fusion is proposed. The scheme is designed to meetvarious function requirement of system. In order to improve the accuracy of sensor measurement data,theBayesian estimation isadopted to fuse the data collected by sensors in the SP370 tire module,which can exclude invalid data and detect faulty sensors. According to the noise contained in measurement data,fusion result is optimized by using Kalman filter,so that the noise signal can be eliminated. Experimental study shows that it can help in handling the problem of limitation by single sensor measurement with the proposed meth-od,meanwhile,it also can suppress the noise introduced by the sensor. By a series of the simulation results,the fea-sibility and reliability of the presented method is validated.关键词
胎压监测系统(TPMS)/数据融合技术/贝叶斯估计/卡尔曼滤波器Key words
tire pressure monitoring system( TMPS) /data fusion technique/Bayesian estimation/Kalman filter.分类
信息技术与安全科学引用本文复制引用
安世奇,由东媛..用于胎压监测系统的一种改进贝叶斯估计数据融合的研究[J].传感技术学报,2018,31(6):915-919,5.
