Abstract
In this study, a novel security-enhanced filter (SEF) is proposed for the system state and malicious attack signal estimation of the stochastic jump-diffusion systems with the external disturbance, measurement noise and malicious attack signal on system and sensor. To efficiently estimate the system state and malicious attack signal by the traditional Luenberger-type filter, a novel smoothed signal model of malicious attack signals is embedded in the system model so that the attack signals in the augmented system do not corrupt the augmented states estimation of SEF again. For the optimal filtering robustness and security, the stochastic multi-objective (MO) $H_{2}/H_{\infty }$ SEF scheme is proposed to achieve optimal disturbance and noise filtering performance and the optimal security enhancement under malicious attack. By using the suboptimal method, the stochastic MO $H_{2}/H_{\infty }$ SEF design could be equivalently transformed to linear matrix inequalities (LMIs)-constrained multi-objective optimization problem (MOP). In the case of nonlinear stochastic system, the MO $H_{2}/H_{\infty }$ SEF design problem could be converted to a Hamilton-Jacobi inequalities (HJIs)-constrained MOP. In order to overcome the difficulty in solving the HJIs-constrained MOP, based on the global linearization technique, the HJIs-constrained MOP for SEF design of nonlinear stochastic systems could be transformed to an LMIs-constrained MOP. Further, an LMIs-constrained multi-objective evolution algorithm (MOEA) is proposed to efficiently solve the LMIs-constrained MOP for the design of SEF. Two simulation examples including the missile trajectory estimation problem by ground radar system under the malicious attack signals and estimation of netwoked-based mass spring system are given to validate the effectiveness of the proposed method.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.