An application of optimized bayesian estimation data fusion algorithm in tire pressure monitoring system

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 Kalman 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.