We consider the problem of estimating the state of a linear time‐invariant Gaussian system in the presence of sparse integrity attacks. The attacker can control p out of m sensors and arbitrarily change the measurements. Under mild assumptions, we can decompose the optimal Kalman estimate as a weighted sum of local state estimates, each of which is derived using only the measurements from a single sensor. Furthermore, instead of the weighted sum approach, we introduce a convex optimization‐based approach to combine the local estimate into a more secure state estimate. It is shown that our proposed estimator coincides with the Kalman estimator with a certain probability when all sensors are benign, and we provide a sufficient condition under which the estimator is stable against the ( p , m ) ‐sparse attack when p sensors are compromised. A numerical example is provided to illustrate the performance of the proposed state estimation scheme.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Secure Dynamic State Estimation with a Decomposing Kalman Filter


    Contributors:
    Liu, Xinghua (author) / Jiang, Rui (author) / Chen, Badong (author) / Sam Ge, Shuzhi (author)


    Publication date :

    2022-09-27


    Size :

    20 pages




    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Extended Kalman Filter vs. Error State Kalman Filter for Aircraft Attitude Estimation

    Madyastha, Venkatesh / Ravindra, Vishal / Mallikarjunan, Srinath et al. | AIAA | 2011


    Extended Kalman Filter vs. Error State Kalman Filter for Aircraft Attitude Estimation

    Madyastha, V. / Ravindra, V. / Mallikarjunan, S. et al. | British Library Conference Proceedings | 2011


    Unscented Kalman filter for vehicle state estimation

    Antonov,S. / Fehn,A. / Kugi,A. et al. | Automotive engineering | 2011


    Extended Kalman Filter for MMS State Estimation

    Markley, F. Landis / Harman, Richard R. / Thienel, Julie K. | NTRS | 2009


    Unscented Kalman filter for vehicle state estimation

    Antonov, S. / Fehn, A. / Kugi, A. | Taylor & Francis Verlag | 2011