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

## TL;DR

This paper introduces a resilient UAV state estimation framework that uses an attack detector and IMU signals during GPS denial, defining an escape time to quantify estimation stability and reliability.

## Contribution

It proposes a new resilience measure called escape time and analyzes the stability of a UAV state estimator under sensor attack conditions.

## Key findings

- The framework maintains estimation errors within tolerable bounds for a quantifiable escape time.
- Simulation results validate the effectiveness of the proposed resilient estimation approach.
- Analytical bounds for escape time provide guarantees on estimation stability during GPS denial.

## 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.