  This paper considers a resilient state estimation framework for unmanned
aerial vehicles (UAVs) that integrates a Kalman filter-like state estimator and
an attack detector. When an attack is detected, the state estimator uses only
IMU signals as the GPS signals do not contain legitimate information. This
limited sensor availability induces a sensor drift problem questioning the
reliability of the sensor estimates. We propose a new resilience measure,
escape time, as the safe time within which the estimation errors remain in a
tolerable region with high probability. This paper analyzes the stability of
the proposed resilient estimation framework and quantifies a lower bound for
the escape time. Moreover, simulations of the UAV model demonstrate the
performance of the proposed framework and provide analytical results.
