基于IMU/GNSS/车速传感器的矿用车定位系统研究

Research on Positioning System of mine Vehicle based on IMU/GNSS/Speed sensor

针对使用低精度惯性测量单元(inertial measurement unit,IMU)的矿用车定位系统存在无法与混合动力能量管理控制策略相结合的问题,本文设计了一种融合IMU、GNSS和车速传感器信号的定位系统。该系统的结构基于直接配置扩展卡尔曼滤波方法,滤波器由运动学模型和传感器误差模型组成。引入车辆车速传感器信号,对车辆车速进行修正,作为预测下一步位置的辅助信息。该体系结构的设计方式使其易于标定不同传感器参数,以便应用于不同定位需求的车辆。同时,为了测试本文所设计系统的性能,选取青岛大学校内路段,采用不同精度的车速传感器对定位系统进行仿真实验,并将仿真实验结果与传统定位方法进行对比。仿真结果表明,融合IMU、GNSS和车速传感器信号的定位系统,定位误差大幅减小,车辆在出现胎压不足或车轮打滑时,定位系统的定位精度依然满足需求,定位精度显著提高,并在车速传感器精度受到干扰的情况下,具有较好的鲁棒性,可较好适应各种恶劣路况,证明本算法是一套稳健可靠的低精度传感器融合定位算法。该研究具有一定的实际应用价值。

Aiming at the problem that the positioning system of mine vehicle using low precision inertial measurement unit(IMU)cannot be combined with the hybrid energy management control strategy,this paper designs a positioning system that integrates IMU,GNSS and speed sensor signals.The structure of the system is based on the direct configuration extended Kalman filter method.The filter is composed of kinematic model and sensor error model.The vehicle speed sensor signal is introduced to correct the vehicle speed as the auxiliary information to predict the next position.The architecture is designed in such a way that it is easy to calibrate different sensor parameters so that it can be applied to vehicles with different positioning requirements.At the same time,in order to test the performance of the system designed in this paper,the road section in Qingdao University was selected in the simulation experiment,and the speed sensor with different accuracy was used to simulate the positioning system,and the simulation results were compared with the traditional positioning method.The simulation results show that the positioning error of the system,which integrates IMU,GNSS and speed sensor signals,is greatly reduced.When the vehicle has low tire pressure or wheel slip,the positioning accuracy of the positioning system still meets the demand,and the positioning accuracy is significantly improved.In addition,it has good robustness when the accuracy of the speed sensor is interfered with,and it can better adapt to all kinds of bad road conditions.This verifies that this algorithm is a practical and robust low precision sensor fusion location algorithm.This research has certain theoretical significance and practical application value.