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Experimental Results on an Integrated GPS and Multisensor System for Land Vehicle Positioning

机译:GPS和多传感器集成系统用于陆地车辆定位的实验结果

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Global position system (GPS) is being widely used in land vehicles to provide positioning information. However, in urban canyons, rural tree canopies, and tunnels, the GPS satellite signal is usually blocked and there is an interruption in the positioning information. To obtain positioning solution during GPS outages, GPS can be augmented with an inertial navigation system (INS). However, the utilization of full inertial measurement unit (IMU) in land vehicles could be quite expensive despite the use of the microelectromechanical system (MEMS)-based sensors. Contemporary research is focused on reducing the number of inertial sensors inside an IMU. This paper explores a multisensor system (MSS) involving single-axis gyroscope and an odometer to provide full 2D positioning solution in denied GPS environments. Furthermore, a Kalman filter (KF) model is utilized to predict and compensate the position errors of the proposed MSS. The performance of the proposed method is examined by conducting several road tests trajectories using both MEMS and tactical grade inertial sensors. It was found that by using proposed MSS algorithm, the positional inaccuracies caused by GPS signal blockages are adequately compensated and resulting positional information can be used to steer the land vehicles during GPS outages with relatively small position errors.
机译:全球定位系统(GPS)被广泛用于陆地车辆以提供定位信息。但是,在城市峡谷,农村树冠和隧道中,GPS卫星信号通常会被阻塞,并且定位信息也会中断。为了在GPS中断期间获得定位解决方案,可以使用惯性导航系统(INS)来增强GPS。但是,尽管使用了基于微机电系统(MEMS)的传感器,但在陆地车辆中使用全惯性测量单元(IMU)可能会非常昂贵。当前的研究集中在减少IMU内部的惯性传感器的数量。本文探讨了一种多传感器系统(MSS),其中包括单轴陀螺仪和里程表,可在被拒绝的GPS环境中提供完整的2D定位解决方案。此外,利用卡尔曼滤波器(KF)模型来预测和补偿提出的MSS的位置误差。通过使用MEMS和战术级惯性传感器进行几次道路测试轨迹来检验所提出方法的性能。发现通过使用提出的MSS算法,可以充分地补偿由GPS信号阻塞引起的位置误差,并且所得到的位置信息可以用于在GPS中断期间以相对较小的位置误差来引导陆地车辆。

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