GPS信号中断时基于偏最小二乘回归的车辆位置预测
首发时间:2014-12-15
摘要:在智能交通系统(ITS)中,车辆的位置预测是至关重要的,而对位置预测的精确度要求更是日益显著。本文提出了一种能够在GPS失效情况下实施的低成本、便利的预测算法。为了在缺乏GPS信号时更好地实现车辆位置的预测,我们利用低成本传感器提供的车辆位置信息,提出了基于窗口的偏最小二乘回归(W-PLSR)算法。该算法消除了窗口外的传感器数据,显著提高了位置的精确度。我们通过在真实的市区中实施道路试验,对算法进行评估,并与一些常用的算法进行比较,如PLSR、基于扩展卡尔曼滤波(EKF)的交互多模型算法(IMM-EKF)等等,结果表明,W-PLSR算法对车辆位置的预测具有更高的精确度,尤其是在GPS中断时。
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Low-cost Sensors Aided Vehicular Position Prediction with Partial Least Squares Regression during GPS Outage
Abstract:Vehicular position prediction is very important in Intelligent Transport Systems (ITS), and the requirements of accuracy for position prediction have been significantly increasing in recent years. In this paper, we focus on designing a more low-cost and convenient method which can operate during GPS outages. In order to better deal with the position prediction during the lack of GPS signals, we introduce a Windowed Partial Least Squares Regression (W-PLSR) approach where vehicle position information from the low-cost sensors was used. Moreover, the window is adjustable and it reduces the step of regression in W-PLSR algorithm. The sensor data outside the window that has nothing to do with the latest position prediction is eliminated. Therefore, the position accuracy can be improved significantly. Finally, the proposed method is evaluated by using road experiments from real urban areas. Compared with the conventional technique such as PLSR and extended Kalman filter combined with an interactive multimodel (IMM-EKF), the results show that WPLSR presents the higher position accuracy especially during the GPS outages.
Keywords: Vehicle Position Prediction GPS Outage WPLSR Sensors
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