Deep Learning-Based Navigation System for Automatic Landing Approach of Fixed-Wing UAVs in GNSS-Denied Environments

Deep Learning-Based Navigation System for Automatic Landing Approach of Fixed-Wing UAVs in GNSS-Denied Environments

Blog Article

The Global Navigation Satellite System (GNSS) is widely used in various applications of UAVs (unmanned aerial vehicles) that require precise positioning or navigation.However, GNSS signals can be blocked in specific environments and are susceptible to jamming and spoofing, which will degrade the performance of navigation systems.In this study, a deep learning-based navigation system for the automatic landing of fixed-wing UAVs in GNSS-denied environments is proposed to serve as an alternative navigation system.

Most visual-based runway landing systems are typically focused on runway detection and localization while neglecting the issue of integrating the localization solution into flight control alphaville clothing and guidance laws to become a complete real-time automatic landing system.This study addresses these problems by combining runway detection and localization methods, YOLOv8 and CNN (convolutional neural network) regression, to demonstrate the robustness of deep learning approaches.Moreover, a line detection method is employed to accurately align the UAV with the runway, effectively resolving issues related to runway contours.

In the control phase, the guidance law and controller are designed to ensure the stable flight of the UAV.Based on a deep learning model framework, this study conducts experiments within the simulation environment, verifying system stability under various assumed conditions, thereby avoiding the risks associated with real-world testing.The simulation results demonstrate that the UAV can achieve automatic landing on 3-degree and 5-degree keychron m4 glide slopes, whether it is directly aligned with the runway or deviating from it, with trajectory tracking errors within 10 m.