Dynamic State Estimation in the Presence of Compromised Sensory Data
Yorie Nakahira, Yilin Mo

Citation
Yorie Nakahira, Yilin Mo. "Dynamic State Estimation in the Presence of Compromised Sensory Data". Conference on Decision and Control 2015, IEEE, 15, December, 2015.

Abstract
In this article, we consider the state estimation problem of a linear time invariant system in adversarial environment. We assume that the process noise and measurement noise of the system are $l_\infty$ functions. The adversary compromises at most $\gamma$ sensors, the set of which is unknown to the estimation algorithm, and can change their measurements arbitrarily. We first prove that if after removing a set of $2\gamma$ sensors, the system is undetectable, then there exists a destabilizing noise process and attacker's input to render the estimation error unbounded. For the case that the system remains detectable after removing an arbitrary set of $2\gamma$ sensors, we construct a resilient estimator and provide an upper bound on the $l_\infty$ norm of the estimation error. Finally, a numerical example is provided to illustrate the effectiveness of the proposed estimator design.

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 Citation formats
• HTML
Yorie Nakahira, Yilin Mo. <a
href="http://www.terraswarm.org/pubs/527.html"
>Dynamic State Estimation in the Presence of Compromised
Sensory Data</a>, Conference on Decision and Control
2015, IEEE, 15, December, 2015.
• Plain text
Yorie Nakahira, Yilin Mo. "Dynamic State Estimation in
the Presence of Compromised Sensory Data". Conference
on Decision and Control 2015, IEEE, 15, December, 2015.
• BibTeX
@inproceedings{NakahiraMo15_DynamicStateEstimationInPresenceOfCompromisedSensory,
author = {Yorie Nakahira and Yilin Mo},
title = {Dynamic State Estimation in the Presence of
Compromised Sensory Data},
booktitle = {Conference on Decision and Control 2015},
organization = {IEEE},
day = {15},
month = {December},
year = {2015},
problem of a linear time invariant system in
adversarial environment. We assume that the
process noise and measurement noise of the system
are $l_\infty$ functions. The adversary
compromises at most $\gamma$ sensors, the set of
which is unknown to the estimation algorithm, and
can change their measurements arbitrarily. We
first prove that if after removing a set of
$2\gamma$ sensors, the system is undetectable,
then there exists a destabilizing noise process
and attacker's input to render the estimation
error unbounded. For the case that the system
remains detectable after removing an arbitrary set
of $2\gamma$ sensors, we construct a resilient
estimator and provide an upper bound on the
$l_\infty$ norm of the estimation error. Finally,
a numerical example is provided to illustrate the
effectiveness of the proposed estimator design.},
URL = {http://terraswarm.org/pubs/527.html}
}


Posted by Yilin Mo on 25 Mar 2015.
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