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《Chinese Journal of Sensors and Actuators》 2018-06
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Research on an Improved Bayesian Estimation Data Fusion for Tire Pressure Monitoring System

AN Shiqi;YOU Dongyuan;College of Automation and Electronic Engineering,Qingdao University of Science and Technology;  
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,the Bayesian 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 method,meanwhile,it also can suppress the noise introduced by the sensor. By a series of the simulation results,the feasibility and reliability of the presented method is validated.
【CateGory Index】: TP212.9
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