飞控与探测2026，Vol.9Issue(4)：62-68,7.DOI:10.20249/j.cnki.2096-5974.2026.04.005
基于LSTM和迁移学习的无人飞行器目标意图预测方法
Method for Aircraft Target Intention Prediction Based on LSTM and Transfer Learning
摘要
Abstract
Unmanned Aerial Vehicles are widely used in various task scenarios due to their minia-turization,unmanned nature,and multifunctionality.To address the issue where traditional deep learning methods cannot quickly and accurately predict UAV intentions under small sample data,a method using Long Short-Term Memory(LSTM)networks and transfer learning is proposed for UAV intention prediction.A virtual dataset,established based on expert experience,is prepro-cessed using SG filtering before being input into the LSTM neural network to obtain a pre-trained model.Finally,the pre-trained model is transferred to actual small sample flight test data,resul-ting in the final LSTM and transfer learning model.Experimental results show that the LSTM and transfer learning model improve prediction accuracy under small sample data.关键词
无人飞行器/LSTM网络/迁移学习/SG滤波/小样本数据集Key words
unmanned aerial vehicle/LSTM network/transfer learning/SG filtering/small sample data-set分类
信息技术与安全科学引用本文复制引用
谈用杰,邵昱轩,陈力恒..基于LSTM和迁移学习的无人飞行器目标意图预测方法[J].飞控与探测,2026,9(4):62-68,7.基金项目
国家自然科学基金(62273114) （62273114）
黑龙江省自然科学基金(YQ2024F015) （YQ2024F015）
中央高校基本科研业务费专项资金资助(3072025YC404) （3072025YC404）
黑龙江省春雁支持计划(CYQN24071) （CYQN24071）
