Aerospace Instrument-Making Annotation << Back Development of a Robust Algorithm for Cooperative Positioning of Leader-Follower Unmanned Aerial Vehicles based on Relative State Estimation Wang Zhong, Neusypin K.A., He Bin, Chen Hao A robust algorithm for cooperative positioning of leader-follower unmanned aerial vehicles (UAVs) based on relative state estimation is proposed. The approach fuses data from an inertial navigation system (INS), a global navigation satellite system (GNSS), and relative measurements between UAVs. An interactive multiple model (IMM) filter is employed for adaptive motion estimation of the follower UAV, while a federated Kalman filter is utilized to integrate multi-source information, enhancing overall positioning accuracy and system robustness. By introducing an adaptive adjustment mechanism for the input noise covariance matrix and generating pseudo-measurements, the system significantly enhances positioning accuracy and stability under GNSS-denied or degraded conditions. Simulation results demonstrate that the proposed approach achieves a notable reduction in the standard deviation of position, velocity, and attitude estimates compared to conventional methods. Keywords: Kalman filter, interactive multiple model algorithm, inertial navigation system, unmanned aerial vehicle, cooperative positioning, federated Kalman filter. DOI: 10.25791/aviakosmos.12.2025.1524 Pp. 14-27.