空天防御2024，Vol.7Issue(3)：94-101,8.
基于改进注意力机制的自适应航迹预测方法
Adaptive Trajectory Prediction Method Based on Improved Attention Mechanism
摘要
Abstract
The existing recurrent neural networks are subject to training overfitting,low prediction accuracy,poor generalization ability,and weak adaptability in solving target trajectory prediction.A target trajectory prediction method using an improved attention mechanism and Gated Recurrent Unit(GRU)was proposed,which could automatically terminate the network training process through an early stopping method to prevent overfitting during training.It saved the optimal network parameters during network training through the model checkpoint function.By introducing an attention mechanism into the GRU network and assigning different weights to trajectory features to focus on key trajectory information,the predictive performance of the network was optimized Finally,simulation experiments results show that the proposed method effectively improves the prediction accuracy,generalization,and adaptability of recurrent neural networks.关键词
航迹预测/注意力机制/早停法/循环神经网络/门控循环单元Key words
trajectory prediction/attention mechanism/early stop method/recurrent neural network/gated recurrent unit分类
信息技术与安全科学引用本文复制引用
黄权印,蔡益朝,李浩,唐晓,王辰洋..基于改进注意力机制的自适应航迹预测方法[J].空天防御,2024,7(3):94-101,8.基金项目
国家自然科学基金青年(61502522) （61502522）
国家社科基金重点(2022-SKJJ-B-056) （2022-SKJJ-B-056）
湖北省自科基金面上项目(2019CFC897) （2019CFC897）
