【摘要】 集成全球定位系统(GPS）和车载传感器信息，提出了一种多模交互滤波算法，使车辆可以适应多种驾驶工况。搭建了纵、侧向运动模型对车辆预期行驶位置进行全覆盖预测性描述；通过数据驱动的神经网络和车辆运动模型实现对GPS信号的可信性评价；利用车辆运动模型之间的交互滤波、概率更新等方式实现对过程误差和测量误差的精准估计。最后，在实车测试环境下进行定位融合算法的验证，结果表明，本文算法能够提升多传感器定位系统的定位精度，并在信号不稳定的情况下持续提供较为准确的位置估计结果。更多还原

【Abstract】 A multi-mode interactive filtering algorithm that integrates Global Positioning System(GPS) and vehicle sensor information was proposed, so that the vehicle can adapt to a variety of driving conditions. The longitudinal and lateral motion model were built to describe the full coverage prediction of the expected driving position of the vehicle. The credibility evaluation of GPS signal was realized by datadriven neural network and vehicle motion model. The accurate estimation of process error and measurement error was realized by interactive filtering and probability updating between vehicle motion models. The positioning fusion algorithm was verified in the real vehicle testing environment. The results show that the proposed algorithm can improve the positioning accuracy of the multi-sensor positioning system and provide more accurate position estimation results in the case of unstable signals.更多还原