信号处理2025，Vol.41Issue(7)：1241-1254,14.DOI:10.12466/xhcl.2025.07.009
基于ADS-B多特征迁移学习的GNSS干扰检测方法
A GNSS Interference Detection Method Utilizing Multi-Feature Transfer Learning from ADS-B Data
摘要
Abstract
The global navigation satellite system(GNSS)serves as a critical foundation for modern aviation systems;however,it is highly vulnerable to radio frequency interference,which can result in flight diversions,go-arounds,or aborted approaches,posing serious risks to aviation safety.automatic dependent surveillance-broadcast(ADS-B),which depends on GNSS for acquiring aircraft position information,is similarly affected when GNSS is subject to radio frequency interference,thereby compromising the availability of ADS-B.Detecting GNSS interference detection based on ADS-B data has become a feasible solution.To overcome the limitations of existing GNSS interference detection models,such as incompatibility with multiple ADS-B versions and inadequate adaptability to China's specific opera-tional environment,this study focuses on analyzing ADS-B data collected during GNSS interference events.This re-search investigates the characteristics under interference conditions,including trajectory fluctuations and variations in navigation quality indicators.By incorporating a sliding window technique,statistical features are dynamically com-puted,and the feature dimensions are expanded to more comprehensively and accurately capture the impact of interfer-ence.A novel GNSS interference detection method is proposed,integrating a long short-term memory-autoencoder(LSTM-AE)with a domain adversarial neural network(DANN).The LSTM-AE extracts features from ADS-B data across different versions and maps them into a unified feature space,ensuring consistent feature representations.The DANN network is subsequently utilized to detect GNSS interference in DO-260A/B version ADS-B data(source do-main),while leveraging DANN's transfer learning capability to adapt to DO-260 version ADS-B data(target domain),thereby enabling efficient cross-version detection.Experimental results indicate that the proposed LSTM-AE-DANN model achieves superior detection performance and robust adaptability across both DO-260 and DO-260A/B version ADS-B datasets.This approach is particularly well suited for China's aviation system requirements and holds substantial practical value.关键词
全球导航卫星系统干扰检测/广播式自动相关监视/长短期记忆自编码器/领域对抗神经网络/迁移学习/航空安全Key words
global navigation satellite system interference detection/automatic dependent surveillance-broadcast/long short-term memory-autoencoder/domain adversarial neural network/transfer learning/aviation safety分类
信息技术与安全科学引用本文复制引用
陈敏,李昊宇,何炜琨,吴仁彪..基于ADS-B多特征迁移学习的GNSS干扰检测方法[J].信号处理,2025,41(7):1241-1254,14.基金项目
国家自然科学基金(U2133204,52272356)The National Natural Science Foundation of China(U2133204,52272356) （U2133204,52272356）
