摘要
针对弱约束非合作目标的轨迹特性和运动特性,提出一种基于LSTM的Encoder-Decoder多步轨迹预测技术(EDMTP)。引入一阶差分处理,降低了轨迹数据的时间依赖性,得到了无趋势的轨迹。构造输入输出的轨迹数据对,将预测问题转化为有监督学习问题,研究多步预测过程中模型性能的变化,实现端到端的轨迹预测。仿真结果表明,该方法能够从历史轨迹数据中提取更多的轨迹特征,在多步轨迹预测中具有明显的优势。与KFTP和HMMTP算法相比,EDMTP的误差增长率分别同比下降了2.18%和3.52%,取得了较好的轨迹预测效果。
Aiming at the trajectory and motion characteristics of weakly constrained non-cooperative targets,a LSTM-based encoder-decoder multi-step trajectory prediction technology(EDMTP)is proposed.The introduction of first-order difference processing reduces the time dependence of the trajectory data,and obtains a trendless trajectory.Constructing an input and output trajectory data pair,transforming the prediction problem into a supervised learning problem,the change of model performance in the multi-step prediction process is studied to realize end-to-end trajectory prediction.Simulation results show that this method can extract more trajectory features from historical trajectory data,and has obvious advantages in multi-step trajectory prediction.Compared with the trajectory prediction algorithms of KFTP and HMMTP,the error growth rate of EDMTP decrease by 2.18%and 3.52%year-on-year,respectively,and achieves better trajectory prediction results.
作者
李青勇
何兵
张显炀
朱晓宇
刘刚
Li Qingyong;He Bing;Zhang Xianyang;Zhu Xiaoyu;Liu Gang(Rocket Force University of Engineering,Xi’an 710025,China)
出处
《航空兵器》
CSCD
北大核心
2021年第2期49-54,共6页
Aero Weaponry
基金
国家自然科学基金青年科学基金项目(61403399)。
关键词
轨迹预测
LSTM
编码器-解码器
监督学习
多步预测
trajectory prediction
LSTM
Encoder-Decoder
supervised learning
multi-step prediction
作者简介
李青勇(1995-),男,甘肃白银人,硕士,研究方向为轨迹分析、轨迹预测;通讯作者:何兵(1983-),男,陕西西安人,副教授,博士,研究方向为人工智能、数据分析、导航制导与控制。
