Coordination and Planning Algorithms for a UAS Fleet in GPS-Denied Environments

Achieving reliable and precise navigation for unmanned aerial vehicles (UAVs) in GPS- denied or GPS-degraded environments remains a critical challenge, particularly when accurate localization and continuous inter-vehicle communication are essential. This paper presents an Extended Kalman Filter (EKF)-based cooperative localization framework that utilizes odometry, heading, and range measurements from onboard sensors and inter-vehicle communication. To enhance localization accuracy and fleet robustness, two novel cooperative navigation strategies are proposed: (i) a landmark-assisted sequential guidance strategy designed to incrementally improve individual UAV localization through strategic assistive positioning, and (ii) a dynamic triangular-formation strategy aimed at preserving fleet cohesion, ensuring robust communication, and maintaining accurate localization through structured motion coordination. Simulation results demonstrate that both approaches significantly enhance UAV fleet localization performance and mission success compared to independent navigation methods, underscoring their effectiveness in complex and uncertain operating conditions