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 out of 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 ‐sparse attack when sensors are compromised. A numerical example is provided to illustrate the performance of the proposed state estimation scheme.
Secure Dynamic State Estimation with a Decomposing Kalman Filter
2022-09-27
20 pages
Article/Chapter (Book)
Electronic Resource
English
Extended Kalman Filter vs. Error State Kalman Filter for Aircraft Attitude Estimation
British Library Conference Proceedings | 2011
|Unscented Kalman filter for vehicle state estimation
Automotive engineering | 2011
|Extended Kalman Filter for MMS State Estimation
NTRS | 2009
|Unscented Kalman filter for vehicle state estimation
Taylor & Francis Verlag | 2011
|