Research On 5G High Precision Positioning Technology For Rail Transit Space Based On Carrier Phase Measurement

With the increasing mileage and passenger volume of rail transit operation in China and the long-term development planning in the field of transportation technology,the comprehensive construction of a smart transportation system,the improvement of train transportation efficiency,the reduction of operating and maintenance costs,and the guarantee of line transportation safety have become the development goals of the rail transit industry today.High-precision positioning services are an important prerequisite for achieving these goals.Under current conditions,existing train positioning technologies have many problems,such as cumulative positioning errors,low positioning accuracy,and high investment and maintenance costs,which cannot meet the requirements for the intelligent development of rail transit systems in the long-term planning.However,in the rail transit scenario,5G carrier phase positioning faces many problems due to factors such as channel interference,tunnel and canyon effects,and high-speed train operation,resulting in slow fixed ambiguity,low success rate,and unstable positioning accuracy.Therefore,how to effectively improve the performance of 5G carrier phase positioning in rail transit space has become one of the hot directions for key technology research in this field.This paper conducts in-depth research on the model and algorithm of carrier phase positioning in rail transit space,focusing on key technology research of 5G cycle slip detection and repair algorithms,carrier phase positioning reference station selection algorithms in high-speed mobile scenarios,and ambiguity resolution algorithms under limited observation time conditions for rail transit space.The main research contents and innovation points are as follows:1.In response to the problem that traditional cycle slip algorithms are insensitive to multi-frequency random cycle slips,single small cycle slips,and multiple consecutive cycle slips in the rail transit scenario,this paper proposes a multi-carrier cycle slip detection algorithm based on timefrequency parameter transfer.By establishing an epoch-difference combination detection model and transferring observation information corresponding to different subcarrier frequencies into time data containing cycle slip information,a 5G time-frequency cycle slip threshold determination model is constructed,which reduces the noise amplification caused by epoch difference and improves the accuracy and success rate of time-frequency cycle slip detection.Simulation results show that the detection accuracy and success rate in small cycle slip,large cycle slip,and consecutive cycle slip detection have been significantly improved compared with traditional algorithms.2.In response to the problems of frequent reference station replacement,poor positioning stability,and low fixed ambiguity efficiency caused by high-speed train movement,it is necessary to perform inter-base station differencing to eliminate the influence of receiver clock bias on fixed ambiguities.However,due to the high-speed movement of the train,the reference station needs to be frequently replaced,which greatly affects the positioning stability of the train by reestablishing the inter-base station differencing equation.Therefore,this paper proposes a reference station selection method based on Doppler relative offset,which uses the Doppler relative offset matrix to establish the objective function and replacement judgment criteria for reference station selection.This method can select the reference station that needs to be replaced,determine the interval of the fixed ambiguity threshold for replacement,and adopt a priority fixing strategy to improve the fixed ambiguity efficiency.Through simulation results comparison with the traditional inter-base station differencing positioning algorithm,the proposed algorithm can greatly reduce the frequency of reference station replacement and effectively improve the positioning stability of the train.3.This article proposes a central iterative optimization-based ambiguity fixing algorithm under the limited observation time condition to address the problem of slow ambiguity fixing speed and low success rate caused by complex situations such as high-speed train movement and signal interference.Based on the operating status of the train terminal,the instantaneous time gap model and short time gap model of the train are established.The range constraint and deviation correction of the position and ambiguity estimation vectors are realized through the position domain state transformation matrix and transformation vector,and the final ambiguity fixing is achieved by combining the central iterative optimization algorithm,thus solving the problem of long ambiguity convergence time and short base station visibility time.Simulation results show that the proposed algorithm has improved ambiguity fixing speed and success rate compared with the LAMBDA algorithm,and can maintain certain advantages even when the clock error increases.