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

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.

💡 Research Summary

The paper addresses the challenging problem of operating a quadrotor UAV on a moving, non‑inertial platform (e.g., a truck or elevator) where both GNSS signals and onboard inertial measurements become unreliable. Conventional state estimators, such as the standard Extended Kalman Filter (EKF), treat platform‑induced accelerations as zero‑mean process noise, which leads to systematic bias, drift, and degraded control performance because the IMU cannot distinguish between the UAV’s own acceleration and that of the carrier.

To overcome this, the authors propose a two‑part solution: (1) an EKF with Unknown Inputs (EKF‑UI) that explicitly augments the state vector with the unknown linear accelerations of the platform, and (2) a cascaded PID controller that uses the EKF‑UI’s position and velocity estimates to generate roll, pitch, and thrust commands for full 3‑D trajectory tracking.

The modeling starts from the full 12‑state Newton‑Euler dynamics of a quadrotor in an inertial frame. By applying relative‑motion equations, the authors derive a simple relation (Equation 4) that separates the UAV’s absolute acceleration from the platform’s linear acceleration (aₓ, a_y, a_z). They then construct a reduced 9‑dimensional extended state ζ =