Towards Resilient UAV: Escape Time in GPS Denied Environment with Sensor Drift
This work addresses the critical issue of maintaining UAV reliability during GPS attacks, though it appears incremental as it builds on existing Kalman filter and attack detection methods.
The paper tackles the problem of resilient state estimation for UAVs in GPS-denied environments with sensor drift by proposing a new resilience measure called escape time, which quantifies a lower bound for the safe duration estimation errors remain tolerable with high probability, as demonstrated through simulations.
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.