Towards Resilient UAV: Escape Time in GPS Denied Environment with Sensor Drift

TL;DR

This paper analyzes the stability of the proposed resilient estimation framework for unmanned aerial vehicles (UAVs) and quantifies a lower bound for the escape time, as the safe time within which the estimation errors remain in a tolerable region with high probability.

Abstract

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.

Figures (7)

• Figure 1: A resilient state estimation framework consisting of GPS attack detection and two modes (normal and emergency) state estimation.
• Figure 2: Illustration of path planning problem considering the increasing uncertainties
• Figure 3: Illustration of the simulation scenario: (1) red line denotes the flight path with normal mode under GPS attack; (2) black line denotes the path with emergency mode.
• Figure 4: State and estimate under the GPS attack at $k=700$.
• Figure 5: Attack detection: Statistics denotes $S_k$ defined in \ref{['e003.1']} of CUSUM detector. The threshold equals to $\frac{\chi_{df}^2(\alpha)}{1-\delta}$ with $\alpha=0.01$ and $\delta=0.15$.