Map-Based Localization Under Adversarial Attacks

  • Yulin YangEmail author
  • Guoquan Huang
Conference paper
Part of the Springer Proceedings in Advanced Robotics book series (SPAR, volume 10)


Due to increasing proliferation of autonomous vehicles, securing robot navigation against malicious attacks becomes a matter of urgent societal interest, because attackers can fool these vehicles by manipulating their sensors, exposing us to unprecedented vulnerabilities and ever-increasing possibilities for malicious attacks. To address this issue, we analyze in-depth the Maximum Correntropy Criterion Extended Kalman Filter (MCC-EKF) and propose a weighted MCC-EKF (WMCC-EKF) algorithm by systematically, rather than in an ad-hoc way, inflating the noise covariance of the compromised measurements based on each measurement’s quality. As a conservative alternative, we also design a secure estimator by first detecting attacks based on \(\ell _0 (\ell _1)\)-optimization assuming that only a small number of measurements can be attacked, and then employ a sliding-window Kalman filter to update the state estimates and covariance using only the uncompromised measurements—the resulting algorithm is termed Secure Estimation-EKF (SE-EKF). Both Monte-Carlo simulations and experiments are performed to validate the proposed secure estimators for map-based localization.



This work was partially supported by the University of Delaware College of Engineering, UD Cybersecurity Initiative, the Delaware NASA/EPSCoR Seed Grant, the NSF (IIS-1566129), and the DTRA (HDTRA1-16-1-0039).


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Authors and Affiliations

  1. 1.Department of Mechanical EngineeringUniversity of DelawareNewarkUSA

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