现代电子技术2026，Vol.49Issue(7)：19-25,7.DOI:10.16652/j.issn.1004-373x.2026.07.004
基于隐马尔科夫模型和LSTM的蜂窝网联无人机轨迹识别技术研究
Cellular networked UAV trajectory recognition technique based on Hidden Markov model and LSTM
摘要
Abstract
In view of the complexity and particularity of the electromagnetic environment in key areas,as well as the powerful concealment of the cellular networked UAV(unmanned aircraft vehicle)and its characteristics that are difficult to be detected by the traditional detection means,a new UAV trajectory recognition method based on the hidden Markov model(HMM)and the long short-term memory(LSTM)neural network is proposed.A large number of signaling data will be generated when cellular users communicate with nearby cellular base stations during operation.Position information is extracted from these signaling data for trajectory kinematics analysis,so as to obtain characteristic parameters,including speed,and the distance from start and to end.The characteristic parameters and road network data within the very area are subjected to road matching based on HMM.The dynamic time warping(DTW)algorithm is used to calculate the similarity characteristics of the trajectory and the road.After comparison,these characteristics are trained as the input data of LSTM,so as to identify suspected UAV users from the many cellular networked users in key security areas.The experimental results show that,when the positioning error is 20 m,the classification recognition accuracy of UAV terminal trajectories and other traffic terminal trajectories are over 90%by selecting the output results of two-classification and six-classification with or without similarity characteristics.The proposed algorithm provides a new idea for the development of UAV detection field.关键词
蜂窝网联无人机/轨迹识别/道路匹配/LSTM/隐马尔科夫/动态时间规整Key words
cellular networked UAV/trajectory recognition/road matching/LSTM/HMM/DTW分类
信息技术与安全科学引用本文复制引用
何想,雷朝军,李增,贾春雷..基于隐马尔科夫模型和LSTM的蜂窝网联无人机轨迹识别技术研究[J].现代电子技术,2026,49(7):19-25,7.基金项目
公安部装备研发计划项目(2024ZB03) （2024ZB03）
中国电科十二所稳定支持资助项目(K2410259) （K2410259）
河北省重点研发计划项目(23370401D) （23370401D）
河北省教改项目(2035GJJG457) （2035GJJG457）
