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. 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
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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2018, Vol. 46 
