Distributed Relative Pose Estimation for Multi-UAV Systems Based on Inertial Navigation and Data Link Fusion

Accurate self-localization and mutual state estimation are essential for autonomous aerial swarm operations in cooperative exploration, target tracking, and search-and-rescue missions. However, achieving reliable formation positioning in GNSS-denied environments remains a significant challenge. This paper proposes a UAV formation positioning system that integrates inertial navigation with data link-based relative measurements to improve positioning accuracy. Each UAV independently estimates its flight state in real time using onboard IMU data through an inertial navigation fusion method. The estimated states are then transmitted to other UAVs in the formation via a data link, which also provides relative position measurements. Upon receiving data link information, each UAV filters erroneous measurements, time aligns them with its state estimates, and constructs a relative pose optimization factor graph for real-time state estimation. Furthermore, a data selection strategy and a sliding window algorithm are implemented to control data accumulation and mitigate inertial navigation drift. The proposed method is validated through both simulations and real-world two-UAV formation flight experiments. The experimental results demonstrate that the system achieves a 76% reduction in positioning error compared to using data link measurements alone. This approach provides a robust and reliable solution for maintaining precise relative positioning in formation flight without reliance on GNSS.