基于多模差分融合的高速车辆定位算法
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(北京邮电大学 电气工程学院,北京 100876)

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姚 锟(1989-),男,甘肃张掖人,硕士,硕士研究生,主要从事车联网、智能交通方向的研究。[FQ)]

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High-speed Vehicle Localization Algorithm Based on Multi-mode Differential Fusion
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(School of Electronic Engineering,BUPT,Beijing 100876,China)

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    摘要:

    针对车辆高速运动下定位延时大、精度低、稳定性差的问题,发挥GPS、RSSI和INS三种定位模式的各自优势,将GPS差分校准算法与RSSI测距相结合求解横坐标,将RSSI测距与INS惯导迭代算法相结合求解纵坐标,提出了一种适合高速运动车辆的多模差分融合精确定位算法;以四车道的高速路为场景进行了仿真实验,当车速为70 km/h时,跟踪误差<1 m,定位延时<0.2 s;实验结果表明,该算法可实现车身级和车道级定位,在精确交通诱导、车辆防撞等智能交通领域具有应用前景和推广价值。

    Abstract:

    Aim to solve the problems of big delay,low accuracy and poor stability of localization under the high-speed situation of vehicles,play all the advantages of three positioning modes,including GPS,RSSI and INS systems. We combine GPS differential algorithm with RSSI ranging together to get abscissa,and fuse RSSI ranging and INS iterative algorithm to get ordinate,propose a multi-mode differential fusion precise localization algorithm for high-speed vehicles. During the simulation experiments based on a four-lane highway scene,the tracking error is less than 1 m,localization delay is less than 0.2 s when the speed is 70 km/h. Experimental results show that the algorithm can achieve lane-level localization accuracy,there are broad promotion prospects in the field of precise intelligent transportation and Vehicle Collision Avoidance System.

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姚锟,任维政.基于多模差分融合的高速车辆定位算法计算机测量与控制[J].,2015,23(7):2464-2466.

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  • 在线发布日期: 2015-07-31
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