Control and State Estimation of Vehicle-Mounted Aerial Systems in GPS-Denied, Non-Inertial Environments
🤖 AI Summary
This work addresses the challenge of state estimation for mobile platforms operating without GNSS and reliable IMU data, where conventional methods fail due to platform acceleration disturbances. The authors propose a robust control and estimation framework that relies solely on external position measurements, explicitly modeling motion-induced disturbances through an Unknown Input Extended Kalman Filter (EKF-UI). This estimator is integrated with a cascaded PID controller to enable high-precision 3D trajectory tracking for quadrotor UAVs. Experimental validation on a mobile cart platform subjected to translational disturbances along the X/Y axes demonstrates significantly improved state estimation stability and tracking accuracy compared to a standard EKF. The approach offers a practical solution for deploying UAVs on dynamic carriers such as trucks or elevators, where platform motion severely compromises conventional navigation systems.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
GPS-denied
non-inertial environments
state estimation
IMU unreliability
quadrotor control
Innovation
Methods, ideas, or system contributions that make the work stand out.
EKF-UI
GNSS-denied navigation
non-inertial environment
external position measurement
quadrotor control
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