UAV Swarm GNSS Spoofing Defense Method Based on Consensus Artificial Potential Field

Abstract

Current global navigation satellite system (GNSS) spoofing defense strategies for unmanned aerial vehicles (UAVs) rely mainly on redundant antennas or inertial navigation systems during the action phase. However, the added payload and accumulated errors they introduce can adversely affect aircraft performance. To address the challenge of effective GNSS spoofing defense for UAVs, a defense method based on a consensus potential field was designed. First, the attack–defense interaction process was established as an artificial potential field problem with unknown repulsive sources. Then, a spatial relationship-based repulsive source prediction method was designed, allowing victim members to calculate the resultant force based on predicted repulsive sources. Finally, a consensus mechanism-based collaborative defense strategy was developed to correct the fight direction, reducing trajectory deviation and destination error. Experimental results show that compared with the game, random decision, particle swarm optimization, and vector proportion-integration-differentiation models, the consensus potential field model has smaller trajectory and destination deviations (12.91 ± 6.41m and 13.94 ± 2.77m, respectively). In addition, the proposed method demonstrates better time performance across different experimental environments than other methods and effectively helps the swarm reach a safe area under navigation deception attacks.