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},
      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.},
      URL = {http://terraswarm.org/pubs/527.html}
  }
  

Posted by Yilin Mo on 25 Mar 2015.
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