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

Abstract

In this paper, an attack-resilient estimation algorithm is presented for linear discrete-time stochastic systems with state and input constraints. It is shown that the state estimation errors of the proposed estimation algorithm are practically exponentially stable.

References in corpus (1)

• Secure estimation and control for cyber-physical systems under adversarial attacks

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