Attack-Resilient Minimum Mean-Squared Error Estimation

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Departmental Papers (CIS)
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CPS Embedded Control
fault tolerance
fault tolerant systems
mean square error methods
estimation
vectors
robustness
noise
Computer Engineering
Computer Sciences
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Abstract

This work addresses the design of resilient estimators for stochastic systems. To this end, we introduce a minimum mean-squared error resilient (MMSE-R) estimator whose conditional mean squared error from the state remains finitely bounded and is independent of additive measurement attacks. An implementation of the MMSE-R estimator is presented and is shown as the solution of a semidefinite programming problem, which can be implemented efficiently using convex optimization techniques. The MMSE-R strategy is evaluated against other competing strategies representing other estimation approaches in the presence of small and large measurement attacks. The results indicate that the MMSE-R estimator significantly outperforms (in terms of mean-squared error) other realizable resilient (and non-resilient) estimators.

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2014-06-01
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Departmental Papers (CIS)
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2023-05-17T15:39:38.000
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2014 American Control Conference (ACC 2014)(http://acc2014.a2c2.org/), Portland, Oregon, June 4-6, 2014
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@INPROCEEDINGS{6859478, author={J. Weimer and N. Bezzo and M. Pajic and O. Sokolsky and I. Lee}, booktitle={2014 American Control Conference}, title={Attack-resilient minimum mean-squared error estimation}, year={2014}, pages={1114-1119}, keywords={control system synthesis;convex programming;least mean squares methods;security of data;stochastic systems;MMSE-R estimator;attack-resilient minimum mean-squared error estimation;conditional mean squared error;convex optimization techniques;semidefinite programming problem;stochastic systems;Estimation;Fault tolerance;Fault tolerant systems;Mean square error methods;Noise;Robustness;Vectors;Estimation;Fault-tolerant systems;Stochastic systems}, doi={10.1109/ACC.2014.6859478}, ISSN={0743-1619}, month={June},}
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