Machine learning and UAV path following identification algorithm based on navigation spoofing

With advances in navigation technology, unmanned aerial vehicles (UAVs) have become widely available to all sections of society. Given the potential hazards of UAVs, as seen from their use in the Russia-Ukraine war and security incidents at London and Baghdad airports, counter-UAV technology is receiving unprecedented attention. This paper describes a method for taking control of a UAV indirectly by navigation spoofing and luring it to a designated area from which it can be neutralized. Our contributions are threefold: first, we analyze the limitations of the traditional state estimation and control model for UAV navigation based on the integration of the Global Navigation Satellite System and Inertial Navigation System. Second, we propose a particle hypothesis and planning model that is insensitive to the particular UAV navigation system, and decompose the UAV navigation spoofing process into two steps: identification and planning. Finally, to overcome the poor heading angle prediction accuracy of unary polynomial regression, we propose a support vector regression algorithm that improves the prediction accuracy from 1.5 & DEG; to 1.01 & DEG; under navigation spoofing. Experimental results using the proposed navigation spoofing method prove that machine learning offers significant advantages in modeless identification.