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

ACTA ELECTRONICA SINICA ›› 2018, Vol. 46 ›› Issue (2) : 418-423.

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ACTA ELECTRONICA SINICA ›› 2018, Vol. 46 ›› Issue (2) : 418-423. DOI: 10.3969/j.issn.0372-2112.2018.02.022

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
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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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QIAO Shao-jie, HAN Nan, ZHU Xin-wen, SHU Hong-ping, ZHENG Jiao-ling, YUAN Chang-an. A Dynamic Trajectory Prediction Algorithm Based on Kalman Filter[J]. Acta Electronica Sinica, 2018, 46(2): 418-423. https://doi.org/10.3969/j.issn.0372-2112.2018.02.022

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Funding

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)
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