TY - GEN
T1 - Attack-resilient Estimation for Linear Discrete-time Stochastic Systems with Input and State Constraints
AU - Wan, Wenbin
AU - Kim, Hunmin
AU - Hovakimyan, Naira
AU - Voulgaris, Petros G.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - In this paper, an attack-resilient estimation algorithm is developed for linear discrete-time stochastic systems with inequality constraints on the actuator attacks and states. The proposed algorithm consists of optimal estimation and information aggregation. The optimal estimation provides minimum-variance unbiased (MVU) estimates, and then they are projected onto the constrained space in the information aggregation step. It is shown that the estimation errors and their covariances from the proposed algorithm are less than those from the unconstrained algorithm. Moreover, we proved that the state estimation errors of the proposed estimation algorithm are practically exponentially stable. A simulation on mobile robots demonstrates the effectiveness of the proposed algorithm compared to an existing algorithm.
AB - In this paper, an attack-resilient estimation algorithm is developed for linear discrete-time stochastic systems with inequality constraints on the actuator attacks and states. The proposed algorithm consists of optimal estimation and information aggregation. The optimal estimation provides minimum-variance unbiased (MVU) estimates, and then they are projected onto the constrained space in the information aggregation step. It is shown that the estimation errors and their covariances from the proposed algorithm are less than those from the unconstrained algorithm. Moreover, we proved that the state estimation errors of the proposed estimation algorithm are practically exponentially stable. A simulation on mobile robots demonstrates the effectiveness of the proposed algorithm compared to an existing algorithm.
UR - http://www.scopus.com/inward/record.url?scp=85082463478&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85082463478&partnerID=8YFLogxK
U2 - 10.1109/CDC40024.2019.9029918
DO - 10.1109/CDC40024.2019.9029918
M3 - Conference contribution
AN - SCOPUS:85082463478
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 5107
EP - 5112
BT - 2019 IEEE 58th Conference on Decision and Control, CDC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 58th IEEE Conference on Decision and Control, CDC 2019
Y2 - 11 December 2019 through 13 December 2019
ER -