Truth-Correction-Based Multisensor Fusion Method for HTS Maglev Train’s Position Detection

High-temperature superconducting (HTS) magnetic levitation (Maglev) holds great promise as a high-speed transportation system of the future. To fully exploit its potential, enhancing localization precision is of utmost importance, necessitating the multifusion of relative and absolute positioning. Currently, there exists a dearth of research on fusion localization tailored specifically for HTS Maglev trains. This article presents a method for multisensor data fusion to detect the position of HTS Maglev trains. The position detection system comprises a tunnel magneto resistance (TMR) sensor, an inertial measurement unit (IMU) module, and an absolute positioning system utilizing radio frequency identification (RFID). By utilizing the three-channel data, a truth-correction extended Kalman filter (EKF) algorithm is developed, which can dramatically reduce the positioning error. The error model and simulation analysis of the subsystem and fusion algorithm are established to accommodate various working conditions. Furthermore, a test platform is constructed to verify the performance of the position detection system. The simulation and experimental results demonstrate that the truth-correction-based multisensor fusion method improves measurement accuracy and meets the requirements of engineering applications.

I. Introduction

The high-temperature superconducting (HTS) magnetic levitation (Maglev) train [1] has emerged as a highly competitive transportation technique in line with the environmental protection developing trend [2]. Based on different principles, the Maglev train can be divided into electrodynamic suspension (EDS) [3], permanent magnetic suspension (PMS) [4], electromagnetic suspension (EMS), and HTS Maglev [5]. HTS Maglev with its unique pinning properties and almost no magnetic resistance, enables stable levitation and guidance, which greatly reduces the energy consumption in the operation process.