An adaptive cubature Kalman filter for nonlinear systems against randomly occurring injection attacks

作者：

Highlights：

• A novel adaptive cubature Kalman filter is proposed for nonlinear system against randomly occurring injection attacks.

• The prior statistical information of the attacks is not required.

• Variation Bayesian method is employed to approximate the joint posterior distribution of state and attack statistics.

• Cubature integral rule is adopted for nonlinear state estimation.

摘要

•A novel adaptive cubature Kalman filter is proposed for nonlinear system against randomly occurring injection attacks.•The prior statistical information of the attacks is not required.•Variation Bayesian method is employed to approximate the joint posterior distribution of state and attack statistics.•Cubature integral rule is adopted for nonlinear state estimation.

论文关键词：State estimation,False data injection attacks,Cubature Kalman filter,Adaptive filtering,Variational Bayesian

论文评审过程：Received 22 September 2021, Revised 12 November 2021, Accepted 24 November 2021, Available online 6 December 2021, Version of Record 6 December 2021.

论文官网地址：https://doi.org/10.1016/j.amc.2021.126834