With the widespread application of global navigation satellite system(GNSS),spoofing attacks pose a threat to the security and reliability of GNSS.It is of great significance to design effective GNSS spoofing detectio...With the widespread application of global navigation satellite system(GNSS),spoofing attacks pose a threat to the security and reliability of GNSS.It is of great significance to design effective GNSS spoofing detection technology to ensure the security and reliability of GNSS system applications for receiver users.Traditional spoofing detection techniques generally only determine whether a spoofing attack has occurred by monitoring the feature changes of one or two data information in the receiver.However,some spoofing modes can cleverly make the monitored data very close to the real data,thus avoiding these detection methods and easily making them ineffective.In this study,a GNSS spoofing jamming detection method based on hybrid kernel relevance vector machine(RVM)is proposed.The improved signal quality monitoring(SQM)movement variance,carrier noise ratio movement variance,pseudo range Doppler consistency,pseudorange residual,Doppler frequency,clock offset and clock drift are used as detection characteristics.This technology can detect GNSS spoofing signals,effectively improving the safety and reliability of GNSS systems.The experimental results show that this technology has high detection accuracy and anti-interference ability and can effectively respond to various forms of spoofing attacks.展开更多
针对具有多维状态变量、多种工作模式和故障模式的复杂工程系统,提出一种基于综合健康指数(synthesized health index,SHI)与相关向量机(relevance vector machine,RVM)的系统级失效预测方法。在离线训练阶段,先根据有限失效历史数据建...针对具有多维状态变量、多种工作模式和故障模式的复杂工程系统,提出一种基于综合健康指数(synthesized health index,SHI)与相关向量机(relevance vector machine,RVM)的系统级失效预测方法。在离线训练阶段,先根据有限失效历史数据建立各工作模式下的健康评估模型,并据此获得各历史退化轨迹的SHI序列;然后再使用RVM对这些序列进行回归处理,进而辨识出与回归曲线最为匹配的函数模型。在线预测阶段,先运用健康评估模型计算当前设备的SHI序列并进行RVM回归,再拟合出离线阶段确定的函数模型并添加时变噪声;最后,外推预测出系统剩余使用寿命的概率密度分布。该方法成功应用到涡轮发动机的失效预测案例。展开更多
基金supported in part by the Key Science and Technology Research of Henan Province(252102210239,242102211029,242102211105)the Henan Provincial Key Laboratory of Smart Lighting Open Fund(2023KF06)+2 种基金Zhumadian Science and Technology Youth Innovation Special Fund(QNZX202407)the Computer Basic Education Teaching Research Project of the National Research Society for Computer Basic Education in Higher Education Institutions(2024-AFCEC-393)the Young Backbone Teacher Support Program of Huanghuai University.
文摘With the widespread application of global navigation satellite system(GNSS),spoofing attacks pose a threat to the security and reliability of GNSS.It is of great significance to design effective GNSS spoofing detection technology to ensure the security and reliability of GNSS system applications for receiver users.Traditional spoofing detection techniques generally only determine whether a spoofing attack has occurred by monitoring the feature changes of one or two data information in the receiver.However,some spoofing modes can cleverly make the monitored data very close to the real data,thus avoiding these detection methods and easily making them ineffective.In this study,a GNSS spoofing jamming detection method based on hybrid kernel relevance vector machine(RVM)is proposed.The improved signal quality monitoring(SQM)movement variance,carrier noise ratio movement variance,pseudo range Doppler consistency,pseudorange residual,Doppler frequency,clock offset and clock drift are used as detection characteristics.This technology can detect GNSS spoofing signals,effectively improving the safety and reliability of GNSS systems.The experimental results show that this technology has high detection accuracy and anti-interference ability and can effectively respond to various forms of spoofing attacks.
文摘针对具有多维状态变量、多种工作模式和故障模式的复杂工程系统,提出一种基于综合健康指数(synthesized health index,SHI)与相关向量机(relevance vector machine,RVM)的系统级失效预测方法。在离线训练阶段,先根据有限失效历史数据建立各工作模式下的健康评估模型,并据此获得各历史退化轨迹的SHI序列;然后再使用RVM对这些序列进行回归处理,进而辨识出与回归曲线最为匹配的函数模型。在线预测阶段,先运用健康评估模型计算当前设备的SHI序列并进行RVM回归,再拟合出离线阶段确定的函数模型并添加时变噪声;最后,外推预测出系统剩余使用寿命的概率密度分布。该方法成功应用到涡轮发动机的失效预测案例。
