Indoor navigation and attitude estimation for UAS using inertial sensors and extended Kalman filtering

Accurate position estimation during navigation is essential for missions conducted using Unmanned Aerial Systems (UASs), particularly in fully autonomous operations or missions performed in Beyond Visual Line of Sight (BVLOS) conditions. In outdoor environments, conventional positioning solutions primarily rely on Global Navigation Satellite Systems (GNSS). However, GNSS-based approaches are not suitable for indoor navigation scenarios or GPS-denied environments, where satellite signals are unavailable or severely degraded. As a result, alternative positioning and navigation technologies are required to ensure reliable and accurate UAS operation in such conditions. Inertial Navigation Systems (INS) are becoming essential components of UAS navigation architectures. By integrating low-cost Inertial Measurement Units (IMU), INS provide reliable navigation and attitude estimation capabilities, particularly in GNSS-denied environments. However, uncompensated bias and drift errors lead to the accumulation of position and velocity errors, resulting in significant navigation inaccuracies. This work investigates the impact of IMU bias and drift errors on UAS navigation and attitude estimation and introduces an estimation technique based on the Extended Kalman Filter (EKF) to jointly estimate and compensate these error sources. The proposed EKF-based approach estimates both bias and drift terms affecting the UAS position and attitude, thereby mitigating the accumulation of velocity and position errors over time. The effectiveness of the proposed method is validated through simulation tests conducted in MATLAB® and Simulink® environments, reproducing realistic operational scenarios. Simulation results demonstrate that the proposed approach significantly enhances UAS navigation performance by improving estimation accuracy and reducing overall navigation errors.