Map-Based Localization Under Adversarial Attacks

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.