Positioning of Suspended Permanent Magnet Maglev Trains Using Satellite-Ground Multisensor Fusion

The reliability and continuity of maglev positioning systems are directly related to the safe operation of trains. To solve the problem that the traditional Kalman filter is easily affected by the process noise, this article uses the particle swarm optimization (PSO) algorithm to improve it and proposes a positioning model of suspended permanent magnet maglev train based on the PSO-Kalman algorithm. The experimental results on 60-m permanent magnet maglev rail transit system technical verification line revealed that compared to the traditional Kalman filter, the mean error (ME), mean relative error (MRE), and root-mean-square error (RMSE) of the PSO-Kalman filter decreased by 38.180%, 23.249%, and 35.838%, respectively. Experiment on the "Redrail" Xingguo line showed that the ME, MRE, and RMSE of PSO-Kalman filter are reduced by 38.280%, 26.994%, and 37.027%, respectively. In addition, as the ability of the global navigation satellite system (GNSS) to generate signal blocking and interference is easily affected by the environment, this study proposed a permanent magnetic levitation train positioning system based on multisensor information fusion using the GNSS, tag electronic beacon, and grating axle counting positioning information. Experiments revealed that compared to the PSO-Kalman fusion method for a single GNSS sensor, the ME, MRE, and RMSE of fusion location reduced by 26.115%, 19.298%, and 20.839%, respectively. This article provides a theoretical basis for solving the positioning problem of permanent magnet maglev trains and provides an important theoretical support for the development of maglev rail transit.