基于多传感器信息融合的轨道交通列车轮径校正方法

Vehicle Wheel Diameter Calibration Method Based on Multi-sensor Information Fusion in Rail Transit

多传感器信息融合是实现轨道交通列车高精度定位的发展趋势。针对列车车轮在运行过程中逐渐磨损导致轮径减小,从而影响轮轴速度传感器测速定位精度的问题,通过分析定位传感器的误差特性,采用轮轴速度传感器、加速度计和多普勒测速雷达构成列车组合定位系统,并结合卡尔曼滤波理论,提出一种基于卡尔曼滤波的轮径预测校正方法。该方法在各传感器工作正常时,通过多传感器信息滤波融合得到列车运动状态参数的最优估计,并完成轮径校正;在辅助传感器失效或故障时,通过过去和当前的传感器量测信息对未来一定时间内的列车运动状态做出定量的预测估计,进而完成轮径的预测与校正。仿真试验结果表明,本文所提出的方法能够达到较高的精度水平,提高了列车组合定位系统的可靠性和自主能力。

Multi-sensor information fusion is the development trend of precise train positioning. Aiming at the problem of train wheel wearing that leads to the decrease of wheel diame- ters and influences the positioning precision of the odometer, the error characteristics of the sensors are analyzed. Then a method is proposed by using accelerometer and Doppler radar aided wheel diameter calibration on the basis of the Kaiman fil- tering and prediction theory. This method could get the opti- mal estimation of train motion state parameters and complete the wheel diameter calibration when sensors work properly; and it could also get the quantitative predictions of the train moving state parameters in a certain time span, by analyzingthe past and present sensor measurement information when the auxiliary sensor fails to work, then complete the wheel diame- ter prediction and calibration. The simulation results show that the proposed method provides high precision and improves the reliability and independent capability of the integrated train po- sitioning system.