Position and Speed Measuring Method of Maglev Train Based on Federal Kalman Filter and Information Fusion

Abstract

The position and speed measuring system is an important part of the High-speed maglev operation control system. The speed and position information accuracy and reliability directly affect the train operation safety. Aiming at the shortcomings and defects of the existing position and speed measuring methods, based on the Federated Kalman filter principle, a multi-source information fusion algorithm is designed to effectively integrate speed and position information from Inertial Navigation System, global satellite navigation system and Doppler radar sensor into the train position and speed measurement. On this basis, a combined position and speed measuring method for maglev train is proposed, it not only has high measurement accuracy, but also has strong robustness and autonomous operation ability. The effectiveness and reliability of the proposed method are experimentally verified based on the MATLAB software simulation platform. The experimental results show that the proposed method can be applied to the real-time position and speed measuring of high-speed maglev.