文献类型：期刊文献

英文题名：Deep Learning-Assisted Unmanned Aerial Vehicle Flight Data Anomaly Detection: A Review

作者：Yang, Lei Li, Shaobo Zhang, Yizong Zhu, Caichao Liao, Zihao

第一作者：Yang, Lei

通信作者：Li, SB[1]

机构：[1]Guizhou Univ, Sch Mech Engn, Guiyang 550025, Peoples R China;[2]Guizhou Inst Technol, Sch Mech Engn, Guiyang 550025, Peoples R China;[3]Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China;[4]Guizhou Univ, State Key Lab Publ Big Data, Guiyang 550025, Peoples R China

第一机构：Guizhou Univ, Sch Mech Engn, Guiyang 550025, Peoples R China

通信机构：corresponding author), Guizhou Inst Technol, Sch Mech Engn, Guiyang 550025, Peoples R China.|贵州理工学院机械工程学院;贵州理工学院;

年份：2024

卷号：24

期号：20

起止页码：31681-31695

外文期刊名：IEEE SENSORS JOURNAL

收录：;EI(收录号：20243717031979);Scopus(收录号：2-s2.0-85203496882);WOS:【SCI-EXPANDED(收录号:WOS:001338604800199)】；

基金：This work was supported inpart by the National Natural Science Foundation of China under Grant 52275480 and in part by the National Natural Science Foundation ofChina under Grant 52365061.

语种：英文

外文关键词：Anomaly detection; Deep learning; flight data; unmanned aerial vehicle (UAV); flight data; unmanned aerial vehicle (UAV)

摘要：Flight data anomaly detection is crucial for ensuring the flight safety of unmanned aerial vehicles (UAVs). By monitoring and analyzing flight data, anomalies can be detected in time to avoid potential risks. Deep learning can automatically extract complex patterns and features from data and has been widely used in UAV flight data anomaly detection in recent years. Given the lack of a comprehensive survey of research related to deep learning in UAV flight data anomaly detection, this article conducts a systematic and in-depth literature review. First, the basic concepts of UAV flight data are briefly introduced, followed by an analysis and summary of the applications of deep learning methods based on prediction and reconstruction in UAV flight data anomaly detection. Emphasis is placed on the research progress of deep learning methods based on recurrent neural network (RNN), convolutional neural network (CNN), auto-encoder (AE), and variational AE (VAE) for UAV flight data anomaly detection. Second, an in-depth analysis of the threshold calculation methods utilized in existing research is conducted and the advantages and limitations of these thresholds in practical applications are discussed. Finally, some insightful research directions are given based on the shortcomings of existing research. This work aims to provide a reference and insight for future research, inspire further studies, and jointly promote the development of this promising field.