• 人工智能与模式识别 •

基于高斯混合-变分自编码器的轨迹预测算法

1. 火箭军工程大学 核工程学院, 西安 710025
• 收稿日期:2019-05-30 修回日期:2019-07-10 发布日期:2019-07-23
• 作者简介:张显炀(1995-),男,硕士研究生,主研方向为机器学习、行为预测;朱晓宇,硕士;林浩申,博士;刘刚,教授、博士;安喜彬,博士。
• 基金项目:
国防科技"引领"基金（18-163-00-75-004-078-01）。

Trajectory Prediction Algorithm Based on Gaussian Mixture-Variational Autoencoder

ZHANG Xianyang, ZHU Xiaoyu, LIN Haoshen, LIU Gang, AN Xibin

1. School of Nuclear Engineering, Rocket Force University of Engineering, Xi'an 710025, China
• Received:2019-05-30 Revised:2019-07-10 Published:2019-07-23

Abstract: The trajectory prediction of warships requires high accuracy and real-time performance,but the high complexity of trajectory data features of warships causes the traditional prediction algorithms to be inaccurate and time-consuming,reducing prediction performance.To address the problem,this paper proposes a warship trajectory prediction algorithm based on Variational Autoencoder(VAE).The trajectory coordinate data set is transformed into a trajectory motion vector set,and the trajectory motion features are extracted and generated by using variational autoencoder.Also,in order to improve the prediction accuracy,the hidden space distribution of the variational autoencoding network is set to be mixture Gaussian distribution,which is closer to the features of real data distribution.Then the classification of trajectory features is accomplished in hidden space to implement end-to-end trajectory prediction.Simulation results show that compared with the traditional trajectory prediction algorithms,GMMTP and VAETP,the proposed algorithm can reduce the prediction error rate by 85.48% and 35.59% respectively.