Research on Multi-Information Fusion Velocity Measurement Algorithm Based on Optimal Estimation and Pattern Recognition

This paper uses information fusion technology to study the problem of immunity and speed accuracy of the Speed measurement system in maglev trains. Firstly, based on the analysis of the principle of inductance proximity switch interference generation, a mathematical analysis of speed measurement accuracy and immunity is carried out. Then a multi-information fusion speed measurement algorithm based on Kalman optimal estimation and pattern recognition is proposed, which can effectively correct the error of MEMS inertial sensor, effectively filter the interference of proximity switch sensor, and effectively dealt with the abnormal situation of speed measurement. This improves the accuracy and immunity of the speed measurement system. Moreover, implementation issues are discussed to provide insight for engineers.