zhw040803-glitch/uav-gps-dqn-detection

This Chinese-language repository is a student project, described in the README as a final-year or master's thesis project. It looks at the problem of GPS spoofing against drones, which means tricking a drone's GPS receiver into believing it is somewhere other than its actual location, and uses a machine learning technique to try to detect and defend against that trickery. The project handles three specific kinds of spoofing attack named in the README. The first is a random attack, where the fake GPS coordinates are scattered without much pattern. The second is a replay attack, where the attacker records real GPS signals and plays them back at the drone later. The third is a stealth attack, which the README labels as the hidden or concealed type, meaning the spoofed coordinates are crafted to look plausible and avoid raising suspicion. The technical stack listed is short. The core algorithm is built with PyTorch and uses a method called DQN, which stands for Deep Q-Network, a reinforcement learning approach where a neural network learns to pick the best action in a given situation. The user interface is built with Tkinter, the basic graphical toolkit that ships with Python, and trajectory plots are drawn with Matplotlib. The program offers four functions through its interface. You can pick which type of attack to simulate, run a no-detection mode that just shows the spoofed flight path, run a DQN-detection mode that tries to spot and counter the attack automatically, and watch the real drone trajectory and the GPS-reported trajectory side by side in real time. Usage is described in a single line. You run main.py and the system starts. The README is short and does not cover installation, training data, model files, or evaluation results, so anyone wanting to reproduce it would need to read the source code or contact the author.