LSTM-Based GNSS Spoofing Detection for Drone Formation Flights

Zheng Wen, Xin Qi, Toshio Sato, Kazuhiko Tamesue, Yutaka Katsuyama, Kazue Sako, Jiro Katto, Takuro Sato

Abstract: In the rapidly evolving logistics industry, drones are becoming indispensable for automated delivery operations. As drone traffic escalates, formation flying is being explored to enhance operational control and increase drone density, thereby reducing the space they occupy. Drones typically rely on Global Navigation Satellite System (GNSS) positioning information for autonomous flight. However, civilian-grade GNSS devices are susceptible to spoofing via Software Defined Radio (SDR), posing significant challenges. In this study, we introduce a novel approach to detect GNSS spoofing by leveraging the multiple GNSS information available from each drone during formation flight. Our investigations, involving two GNSS receivers spoofed by an SDR, reveal that spoofing results in a calculated distance between two receivers that is smaller than the actual value. Capitalizing on this characteristic, we designed simulations of formation flights involving two and five drones. We also developed a GNSS spoofing detection method using the Long Short-Term Memory (LSTM) network. The performance of our spoof detection method was evaluated using simulation data. The results demonstrate that using multiple GNSS data from drones in formation flight significantly enhances performance, achieving an F1 score of 0.96 or higher. This study underscores the potential of our proposed method in improving the security and reliability of drone operations.