多传感器融合与邻居协作的车辆精确定位方法

发布时间：2018-06-02 07:42

  本文选题：车辆定位 + 多传感器融合　； 参考：《电子技术应用》2017年06期

【摘要】：针对现有车辆定位装置定位精度不高的问题,提出一种面向车辆自组织网络的车辆精确定位方法。首先,获取车辆上的多传感器信息,融合这些信息构建当前车辆的状态模型;然后,采用贝叶斯滤波方法计算车辆当前状态的可信度;接着,结合当前车辆的一跳邻居车辆信息估算其相对位置;最后,综合上述信息修正车辆的当前位置,提高车辆定位精度。实验表明,与常用的全球定位系统(GPS)、扩展卡尔曼滤波方法相比,该方法的定位精度高,且受GPS定位误差的影响小。
[Abstract]:In order to solve the problem that the positioning accuracy of existing vehicle positioning devices is not high, a vehicle positioning method for vehicle self-organizing network is proposed. Firstly, the multi-sensor information on the vehicle is obtained, and the state model of the current vehicle is constructed by fusion of the information. Then, the credibility of the current state of the vehicle is calculated by using Bayesian filtering method. Combined with the one-hop neighbor vehicle information of the current vehicle, the relative position of the vehicle is estimated. Finally, the vehicle location accuracy is improved by synthesizing the above information to correct the current position of the vehicle. The experimental results show that compared with the conventional GPS and extended Kalman filter, the proposed method has a high positioning accuracy and is less affected by the GPS positioning error.
【作者单位】： 江苏开放大学信息与机电工程学院;南京信息工程大学电子与信息工程学院;
【分类号】：TN929.5;U495
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本文编号：1967974