Control and State Estimation of Vehicle-Mounted Aerial Systems in GPS-Denied, Non-Inertial Environments

TL;DR

This paper introduces a robust control and estimation framework for quadrotors operating in GPS-denied, non-inertial environments, using external measurements and an EKF-UI to improve stability and tracking.

Contribution

It presents a novel estimation method that relies solely on external position data and an EKF-UI, enabling quadrotors to operate reliably without inertial sensors in challenging environments.

Findings

Significantly improved stability over standard EKF.

Enhanced trajectory tracking accuracy.

Validated in a moving-cart testbed with dissonant axes.

Abstract

We present a robust control and estimation framework for quadrotors operating in Global Navigation Satellite System(GNSS)-denied, non-inertial environments where inertial sensors such as Inertial Measurement Units (IMUs) become unreliable due to platform-induced accelerations. In such settings, conventional estimators fail to distinguish whether the measured accelerations arise from the quadrotor itself or from the non-inertial platform, leading to drift and control degradation. Unlike conventional approaches that depend heavily on IMU and GNSS, our method relies exclusively on external position measurements combined with a Extended Kalman Filter with Unknown Inputs (EKF-UI) to account for platform motion. The estimator is paired with a cascaded PID controller for full 3D tracking. To isolate estimator performance from localization errors, all tests are conducted using high-precision motion capture systems. Experimental results in a moving-cart testbed validate our approach under both translational in X-axis and Y-axis dissonance. Compared to standard EKF, the proposed method significantly improves stability and trajectory tracking without requiring inertial feedback, enabling practical deployment on moving platforms such as trucks or elevators.