Research on Positioning Deception of Multi-Rotor UAV Based on Adaptive Neural Network Kalman Filtering Algorithm

With the gradual popularization of unmanned aerial vehicles (UAVs) and the convenience of the people, it also brings convenience to the society, such as the frequent occurrence of black flight incidents of UAVs, which affects people's normal life and work order, such as privacy issues, safety risks, airspace management, noise pollution, and social security. In order to solve the above problems, the UAV GPS receiving model and the UAV GPS deception model are established, and based on the consideration of objective conditions, the neural network adaptive Kalman filter algorithm is integrated, which can effectively reduce the interference of external noise on the operation of the whole system. Added inverse
$\omega-k$
algorithm based on HackRf One, Spoofing signals can be generated quickly and efficiently. The method is controlled by the PC, the data is fitted in HackRf One, and then the received location information and output GPS output location information are corrected through the BP neural network, and finally the interference and deception signal is transmitted through a specific antenna. Experiments have proved that the HackRf One deception drone can effectively deceive the GPS of the UAV and ensure the safety of the UAV interception system.