Departmental Papers (CIS)

Date of this Version

6-2014

Document Type

Conference Paper

Comments

2014 American Control Conference (ACC 2014), Portland, Oregon, June 4-6, 2014

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.

Subject Area

CPS Embedded Control

Publication Source

2014 American Control Conference (ACC 2014)

Start Page

1114

Last Page

1119

DOI

10.1109/ACC.2014.6859478

Copyright/Permission Statement

© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Keywords

fault tolerance, fault tolerant systems, mean square error methods, estimation, vectors, robustness, noise

Bib Tex

@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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Date Posted: 11 October 2016

This document has been peer reviewed.