Research On Dynamic Attitude Monitoring Technology Of High Speed Train Under Crosswind

With the rapid development of China's high-speed railway and urban rail transit,in order to meet the high-speed operation of modern trains,The train control system needs a set of low-cost and reliable train attitude measurement system.MEMS(Micro-Electro-Mechanical system)IMU(Inertial Measurement Unit)has the characteristics of low cost and low power consumption.At present,the domestic high-speed train ATO(Automatic Train Operation)is realized on the basis of CTCS3 train control system,and has been installed and operated successfully at present.The realization of ATO is mainly to calculate the train position by measuring the attitude information of the train by IMU.Speed and other information,such as GNSS and radar sensors for multi-sensor information fusion,train information for reliable measurement.The relevant information is sent to train control system for decision-making control.The ATO system of CTCS level 3 is suitable for high-speed grade lines,and it is an important research direction for future development to expand its adaptability to more complex environment.In this article,the dynamic attitude monitoring technology of high-speed train under the condition of lateral wind is studied,including mathematical modeling of sensor data processing,sensor error analysis,algorithm compensation,attitude calculation and experimental analysis.The specific research methods and innovations are as follows:1.A model that can suppress signal divergence and improve solution accuracy is proposed.The attitude information solved by the IMU solution will be diverged as the posture information is solved over time.In order to suppress the divergence of the signal,the input and output signals form a closed loop,so that the output signal feeds back the input signal in real time.A strong tracking self-feedback model based on RLS multiple wavelet decomposition reconstruction is proposed.2.Perform error analysis and algorithm compensation on the sensor.Based on the analysis of the sensor error source,the noise of the signal is filtered and error compensated.The algorithm is based on wavelet transform and improved to establish a new soft threshold function.Since the data processed by the model has some singular values,an improved fast median filtering algorithm is proposed.For the problem of gyro zero-bias noise,a zero-bias stability suppression algorithm is proposed.3.Perform attitude calculation and experimental analysis.Two sets of experiments were carried out,namely dynamic experiment and static test.For the two different experiments,the proposed algorithm model uses different parameters and methods to measure the improved sensor performance.Finally,the results of this paper are compared with the results of the latest wavelet transform algorithm.The results show that the algorithm reduces the noise in the signal,effectively suppresses the random drift of the MEMS gyroscope and improves the attitude solution.Precision.It is affirmed the feasibility and effectiveness of using this method to remove the signal noise of the gyroscope output and improve the accuracy of the use.Finally,the Bao-Lan line is selected to monitor the train attitude in some places where the cross wind is obvious,such as canyons,bridges and tunnel entrances.The effectiveness and feasibility of the proposed method are proved,and some reference values are provided for the train attitude monitoring.