This article presents an up-to-date tutorial review of nonlinear Bayesian estimation. State estimation for nonlinear systems has been a challenge encountered in a wide range of engineering fields, attracting decades o...This article presents an up-to-date tutorial review of nonlinear Bayesian estimation. State estimation for nonlinear systems has been a challenge encountered in a wide range of engineering fields, attracting decades of research effort. To date,one of the most promising and popular approaches is to view and address the problem from a Bayesian probabilistic perspective,which enables estimation of the unknown state variables by tracking their probabilistic distribution or statistics(e.g., mean and covariance) conditioned on a system's measurement data.This article offers a systematic introduction to the Bayesian state estimation framework and reviews various Kalman filtering(KF)techniques, progressively from the standard KF for linear systems to extended KF, unscented KF and ensemble KF for nonlinear systems. It also overviews other prominent or emerging Bayesian estimation methods including Gaussian filtering, Gaussian-sum filtering, particle filtering and moving horizon estimation and extends the discussion of state estimation to more complicated problems such as simultaneous state and parameter/input estimation.展开更多
由于工业控制系统(industrial control system,ICS)与物理环境紧密联系,其特有的序列攻击可通过将合法的操作注入到操作序列中的不合理位置上,迫使ICS进入异常状态,损毁设备,甚至破坏生态环境.目前,针对序列攻击检测的研究基本上是从信...由于工业控制系统(industrial control system,ICS)与物理环境紧密联系,其特有的序列攻击可通过将合法的操作注入到操作序列中的不合理位置上,迫使ICS进入异常状态,损毁设备,甚至破坏生态环境.目前,针对序列攻击检测的研究基本上是从信息流中提取操作序列进行检测,易受错误、虚假数据等情况的影响,导致检测精度受到限制.针对该问题,充分考虑ICS的操作与物理环境的相互依赖性,提出一种双流融合的工业控制异常检测机制,从物理环境中实时提取工业控制设备的状态信息组成设备状态流,并将其与信息流相融合,从操作次序和时序2个维度检测操作序列是否正常.同时利用设备状态流信息识别操作间隔中的工业控制设备的异常状态,提升异常检测范围和对操作时序异常的检测精度.实验结果表明:该方法能有效地识别序列攻击和部分工业控制设备的异常状态.展开更多
文摘This article presents an up-to-date tutorial review of nonlinear Bayesian estimation. State estimation for nonlinear systems has been a challenge encountered in a wide range of engineering fields, attracting decades of research effort. To date,one of the most promising and popular approaches is to view and address the problem from a Bayesian probabilistic perspective,which enables estimation of the unknown state variables by tracking their probabilistic distribution or statistics(e.g., mean and covariance) conditioned on a system's measurement data.This article offers a systematic introduction to the Bayesian state estimation framework and reviews various Kalman filtering(KF)techniques, progressively from the standard KF for linear systems to extended KF, unscented KF and ensemble KF for nonlinear systems. It also overviews other prominent or emerging Bayesian estimation methods including Gaussian filtering, Gaussian-sum filtering, particle filtering and moving horizon estimation and extends the discussion of state estimation to more complicated problems such as simultaneous state and parameter/input estimation.
文摘由于工业控制系统(industrial control system,ICS)与物理环境紧密联系,其特有的序列攻击可通过将合法的操作注入到操作序列中的不合理位置上,迫使ICS进入异常状态,损毁设备,甚至破坏生态环境.目前,针对序列攻击检测的研究基本上是从信息流中提取操作序列进行检测,易受错误、虚假数据等情况的影响,导致检测精度受到限制.针对该问题,充分考虑ICS的操作与物理环境的相互依赖性,提出一种双流融合的工业控制异常检测机制,从物理环境中实时提取工业控制设备的状态信息组成设备状态流,并将其与信息流相融合,从操作次序和时序2个维度检测操作序列是否正常.同时利用设备状态流信息识别操作间隔中的工业控制设备的异常状态,提升异常检测范围和对操作时序异常的检测精度.实验结果表明:该方法能有效地识别序列攻击和部分工业控制设备的异常状态.