Research On Integrate Train Postioning Based On GPS/INS And Balise

Since the high-speed railway in China since the construction, a large number of new lines began to operate, in the convenience of national travel also led to the socio-economic development. But with the high-speed train running speed also to the train operation control system has brought challenges, in order to ensure high-speed trains can be safe and efficient operation, so the train positioning system can provide high-precision, high reliability of the positioning information and anti-performance. The train combination positioning system with multiple sensors can give full play to the performance advantages of each sensor, and improve the system fault tolerance. Therefore, this paper focuses on the train combination positioning method based on GPS and key responder, and focuses on the fusion estimation algorithm of train combination system.This paper first introduces the research status of multi-sensor train combination positioning system at home and abroad, and puts forward the train combination method of GPS \ INS and key transponder in combination with the traditional GPS\INS combination mode, taking into account the characteristics of each sensor And improve the positioning accuracy and redundancy of the combined system. The key technology development of the information fusion in train combination location is studied.In order to deal with the external uncertainties of the train positioning system in the complex operation scene, the robust estimation theory is introduced to improve the algorithm. In this paper, the multi-sensor information fusion of the integrated positioning system is carried out by using the volumetric Kalman algorithm. , The improved algorithm has a good resistance to unknown interference. At the same time, in order to solve the potential non-definite problem of the improved algorithm in the iterative computation process, the singular value decomposition is used to replace the original Golleski decomposition, so that the algorithm can be used in the computer Application is better applied. The improved Kalman robustness algorithm based on singular value decomposition has a great improvement in robustness, convergence and precision.This paper studies the combination of the integrated navigation system and the filtering structure, designs the system structure of the train combination positioning system in this paper, and improves the algorithm for the federated filtering structure. The state equation and the measurement equation of the combined system are analyzed and established. Of the simulation design to lay the foundation.Finally, the paper analyzes the train combination based on GPS\INS and key transponders. The performance of the improved algorithm in terms of accuracy and robustness and the performance of single positioning and combined positioning are verified by simulation. The simulation results show that the improved algorithm can effectively reduce the positioning error of the combined positioning system and improve it in dealing with external uncertain interference.