A Dynamic Trajectory Prediction Algorithm Based on Kalman Filter

QIAO Shao-jie; HAN Nan; ZHU Xin-wen; SHU Hong-ping; ZHENG Jiao-ling; YUAN Chang-an

doi:10.3969/j.issn.0372-2112.2018.02.022

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A Dynamic Trajectory Prediction Algorithm Based on Kalman Filter

更新时间：2025-07-16

- A Dynamic Trajectory Prediction Algorithm Based on Kalman Filter
- Acta Electronica Sinica Vol. 46, Issue 2, Pages: 418-423(2018)
- 作者机构：
  
  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
  6. 成都信息工程大学网络空间安全学院,四川,成都,610225
  7. 成都信息工程大学管理学院,四川,成都,610103
  8. 西南交通大学信息科学与技术学院,四川,成都,611756
  9. 成都信息工程大学软件工程学院,四川,成都,610225
  10. 广西师范学院计算机与信息工程学院,广西,南宁,541004
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61772091, No.61363037);Planning Fund of Humanities and Social Science Research Projects of Ministry of Education of China (No.15YJAZH058);Youth Fund of Humanities and Social Science Research Projects of Ministry of Education of China (No.14YJCZH046);Funded by Education Department of Sichuan Province (No.14ZB0458);Talent Introduction Project of Chengdu University of Information Technology (No.KYTZ201715, No.KYTZ201750);Supported by Research and Innovation Team Construction Project of Sichuan University (No.18TD0027);Science Research Foundation for Young Academic Leaders of Chengdu University of Information Technology (No.J201701)
- DOI：10.3969/j.issn.0372-2112.2018.02.022
  CLC： TP311
- Published Online：25 February 2018，
  
  Published：2018
- 稿件说明：
移动端阅览
QIAO Shao-jie, HAN Nan, ZHU Xin-wen, et al. A Dynamic Trajectory Prediction Algorithm Based on Kalman Filter[J]. Acta Electronica Sinica, 2018, 46(2): 418-423.
DOI：

QIAO Shao-jie, HAN Nan, ZHU Xin-wen, et al. A Dynamic Trajectory Prediction Algorithm Based on Kalman Filter[J]. Acta Electronica Sinica, 2018, 46(2): 418-423. DOI： 10.3969/j.issn.0372-2112.2018.02.022.

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.

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.

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