An Application of Optimized Bayesian Estimation Data Fusion Algorithm in Tire Pressure Monitoring System

被引：0
|
作者：
Cao, Menglong [1 ]
You, Dongyuan [1 ]
机构：
[1] Qingdao Univ Sci & Technol, Qingdao 266061, Peoples R China
来源：
PROCEEDINGS OF THE 30TH CHINESE CONTROL AND DECISION CONFERENCE (2018 CCDC) | 2018年
关键词：
Tire pressure monitoring system; Data fusion; Bayesian estimation; Kalman filter;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In order to improve the accuracy of the pressure and temperature data measured in the tire pressure monitoring system(TPMS), an optimized Bayesian estimation data fusion method is proposed. The systematic scheme is designed, which can fulfill the requirements of system function. The Bayesian estimation is used to fuse the multi-sensor data to reduce the uncertainty of the measurement. Combining the Kahnan filter eliminates the noise signal to obtain reliable data information. Experimental results show that the proposed algorithm can effectively suppress noise and take precise pressure and temperature values.
引用
收藏
页码：6564 / 6568
页数：5
相关论文
共 50 条
  • [1] Technology of data fusion for automobile tire pressure monitoring system
    Liu Jianxin
    Ping, Tan
    [J]. PROCEEDINGS OF THE INTERNATIONAL CONFERENCE INFORMATION COMPUTING AND AUTOMATION, VOLS 1-3, 2008, : 1278 - +
  • [2] Bayesian fusion algorithm for improved oscillometric blood pressure estimation
    Forouzanfar, Mohamad
    Dajani, Hilmi R.
    Groza, Voicu Z.
    Bolic, Miodrag
    Rajan, Sreeraman
    Batkin, Izmail
    [J]. MEDICAL ENGINEERING & PHYSICS, 2016, 38 (11) : 1300 - 1304
  • [3] Traffic Data Collection Using Tire Pressure Monitoring System
    Savic, Nemanja
    Junghans, Marek
    Krstic, Milos
    [J]. TELEMATICS - SUPPORT FOR TRANSPORT, 2014, 471 : 19 - 28
  • [4] An optimized motion estimation algorithm and application in the FRUC system
    Deng, Min-Jun
    Gan, Ping
    Chen, Zhuo
    Shen, Xiao-Qing
    Li, Dong-Lian
    Yu, Ming-Yan
    Zhang, Yu
    Zeng, Cai-Lan
    Huang, He
    [J]. International Journal of Multimedia and Ubiquitous Engineering, 2015, 10 (08): : 221 - 230
  • [5] Intelligent Tire Based Pressure Monitoring Algorithm
    Khaleghian, Seyedmeysam
    Taheri, Saied
    [J]. PROCEEDINGS OF THE ASME INTERNATIONAL MECHANICAL ENGINEERING CONGRESS AND EXPOSITION, 2017, VOL 12, 2018,
  • [6] Research of Tire Pressure Monitoring System
    Yuan, Xu
    [J]. PROCEEDINGS OF THE 2016 2ND WORKSHOP ON ADVANCED RESEARCH AND TECHNOLOGY IN INDUSTRY APPLICATIONS, 2016, 81 : 746 - 749
  • [7] Tire Pressure Monitoring System for Trucks
    Zhou, Yulan
    Chai, Yongsheng
    Wang, Yantao
    [J]. 2008 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-11, 2008, : 1743 - 1745
  • [8] Research on tire pressure monitoring system
    Zhang, Yuan-Yong
    Wu, Zhong-Fu
    Li, Hua
    [J]. Yadian Yu Shengguang/Piezoelectrics and Acoustooptics, 2006, 28 (03): : 265 - 268
  • [9] Estimation of Tire Stiffness Variation based on Adaptive Extended Kalman Filter of Suspension Systems and Its Application to Indirect Tire Pressure Monitoring System
    Lee, Dong-Hoon
    Kim, Gi-Woo
    [J]. SENSORS AND SMART STRUCTURES TECHNOLOGIES FOR CIVIL, MECHANICAL, AND AEROSPACE SYSTEMS 2020, 2020, 11379
  • [10] Estimation of Road Roughness Based on Tire Pressure Monitoring
    Zeng, Qing
    Hu, Xiaoyang
    Shi, Xiaodong
    Ren, Yiting
    Li, Yuan
    Duan, Zhongdong
    [J]. INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS, 2022, 22 (06)
12345