Attack-resilient Estimation for Linear Discrete-time Stochastic Systems with Input and State Constraints

It addresses the problem of secure state estimation under attacks for constrained linear stochastic systems, which is relevant for safety-critical control applications.

The paper proposes an attack-resilient estimation algorithm for linear discrete-time stochastic systems with state and input constraints, and proves that the state estimation errors are practically exponentially stable.