电子学报 ›› 2018, Vol. 46 ›› Issue (2): 418-423.DOI: 10.3969/j.issn.0372-2112.2018.02.022

• 学术论文 • 上一篇    下一篇

基于卡尔曼滤波的动态轨迹预测算法

乔少杰1, 韩楠2, 朱新文3, 舒红平4, 郑皎凌4, 元昌安5   

  1. 1. 成都信息工程大学网络空间安全学院, 四川成都 610225;
    2. 成都信息工程大学管理学院, 四川成都 610103;
    3. 西南交通大学信息科学与技术学院, 四川成都 611756;
    4. 成都信息工程大学软件工程学院, 四川成都 610225;
    5. 广西师范学院计算机与信息工程学院, 广西南宁 541004
  • 收稿日期:2016-07-15 修回日期:2017-10-09 出版日期:2018-02-25 发布日期:2018-02-25
  • 通讯作者: 韩楠
  • 作者简介:乔少杰,男,1981年生于黑龙江省.2009年毕业于四川大学,获博士学位.现为成都信息工程大学网络空间安全学院教授.主要研究方向为轨迹大数据挖掘、轨迹预测等.E-mail:sjqiao@cuit.edu.cn
  • 基金资助:
    国家自然科学基金(No.61772091,No.61363037);教育部人文社会科学研究规划基金(No.15YJAZH058);教育部人文社会科学研究青年基金(No.14YJCZH046);四川省教育厅资助科研项目(No.14ZB0458);成都信息工程大学引进人才科研启动项目(No.KYTZ201715,No.KYTZ201750);四川高校科研创新团队建设计划资助(No.18TD0027);成都信息工程大学中青年学术带头人科研基金(No.J201701)

A Dynamic Trajectory Prediction Algorithm Based on Kalman Filter

QIAO Shao-jie1, HAN Nan2, ZHU Xin-wen3, SHU Hong-ping4, ZHENG Jiao-ling4, YUAN Chang-an5   

  1. 1. School of Cybersecurity, Chengdu University of Information Technology, Chengdu, Sichuan 610225, China;
    2. School of Management, Chengdu University of Information Technology, Chengdu, Sichuan 610103, China;
    3. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, Sichuan 611756, China;
    4. School of Software Engineering, Chengdu University of Information Technology, Chengdu, Sichuan 610225, China;
    5. School of Computer and Information Engineering, Guangxi Teachers Education University, Nanning, Guangxi 541004, China
  • Received:2016-07-15 Revised:2017-10-09 Online:2018-02-25 Published:2018-02-25

摘要: 基于拟合的传统轨迹预测算法已无法满足高精度和实时性预测要求.提出基于卡尔曼滤波的动态轨迹预测算法,对移动对象动态行为进行状态估计,利用前一时刻的估计值和当前时刻的观测值更新对状态变量的估计,进而对下一时刻的轨迹位置预测.大量真实移动对象数据集上的实验结果表明:GeoLife数据集上基于卡尔曼滤波的轨迹预测算法的平均预测误差(预测轨迹点与实际轨迹点的均方根误差)为12.5米;与基于轨迹拟合的轨迹预测算法相比,T-Drive数据集预测误差平均下降了555.4米,预测准确率提升了7.1%.在保证预测时效性前提下,基于卡尔曼滤波的动态轨迹预测算法解决了轨迹预测精度较低的问题.

关键词: 移动对象数据库, 状态估计, 轨迹预测, 卡尔曼滤波, 轨迹拟合

Abstract: Traditional fitting-based trajectory prediction algorithms cannot meet the requirements of high accuracy and real-time prediction.A dynamic Kalman filter based TP approach was proposed,which performs state estimation of dynamic behavior with regard to moving objects,updates the state variable estimation value based on the estimation of the previous and current observation states,in order to infer the next location of moving objects.Extensive experiments are conducted on real datasets of moving objects and the results demonstrate that the average prediction error (root mean square error between the predicted location and the actual location) of the TP algorithm based on Kalman filter is around 12.5 meters on the GeoLife datasets.The prediction error is reduced by about 555.4 meters by compared to the fitting-based TP algorithms,and the prediction accuracy is increased by 7.1% on the T-Drive datasets as well.The dynamic TP approach based on Kalman filter can handle the problem of low prediction accuracy with the guarantee of efficient time performance.

Key words: moving objects databases, state estimation, trajectory prediction, Kalman filter, trajectory fitting

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