RAIM Technology Based On Multi-constellation GNSS In Train Application Research In Positioning

With the rapid development of high-speed railway and satellite positioning technology,train positioning based on satellite navigation systems has received more and more attention.It can provide trains with 24-hour and all-weather navigation and positioning services.When satellite navigation system is used as the main positioning method for trains,in addition to providing basic positioning services to train users,the navigation system also needs to be able to timely send an alarm to train users when it fails.Receiver Autonomous Integrity Monitoring(RAIM)technology has the characteristics of no external equipment,fast detection and response speed,and easy implementation.It has integrity monitoring capabilities,which can ensure the safety of positioning.The external environment of train operation is complex and variable,and requires high availability and fault detection performance of the RAIM algorithm.Therefore,studying the application of multi constellation RAIM technology in train positioning has important practical research significance.Based on the needs of train positioning integrity monitoring,combined with the current research situation at home and abroad,this paper applies multi constellation RAIM technology to the railway field,and improves the availability and fault detection performance of RAIM technology in train positioning through improved algorithms to meet the needs of railway transportation.The main research work of this paper is as follows:Firstly,the existing train integrity monitoring methods at home and abroad,the positioning requirements for high-speed railways,and the principles and processes of train positioning and RAIM algorithms are analyzed.The visible star judgment method,horizontal protection level and key technical parameters of RAIM technology are described.Secondly,aiming at the problem of RAIM coverage holes caused by a small number of visible stars or poor geometric distribution structure in train positioning,a program flow for applying multi constellation combined RAIM to train positioning was designed,and the sources of navigation and positioning information errors and the time and coordinate benchmarks of each constellation of the Global Navigation Satellite System(GNSS)were analyzed.On this basis,the GNSS observation information is fused to construct multiple constellation observation equations,and detection statistics are constructed using the least square residual method and the parity vector method to detect and identify multiple constellation single satellite faults.Simulation experiments are conducted on the multiple constellation RAIM algorithm using train positioning data to verify the availability and effectiveness of the algorithm.Finally,based on the analysis of the major missed detections and false alarms that can occur when using traditional parity vector methods to identify dual star faults,an improved dual star fault identification method is proposed.The method uses parity vectors to construct multiple constellation fault feature planes,and utilizes the spatial position relationship between the fault feature planes and parity vectors to identify dual star faults without selecting the identification threshold,avoiding its impact on the fault identification rate.The experimental results show that the proposed algorithm can significantly improve the recognition rate of dual satellite faults,and the recognition performance in multi constellation systems is better than that in single constellation systems,making the system more reliable under the same fault conditions,and effectively improving the integrity monitoring performance of the train navigation system.