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Unmanned aerial vehicles (UAVs) are increasingly used in military and scientific research. UAVs rely on accurate location information for a variety of purposes including navigation, motion planning and control, and mission completion. UAV INS/GPS localization is generally used based on navigation filter. Extended Kalman filter is largely used to solve the problem of data fusion and localization, however, EKF suffers from the initialization problem and the linearization errors which severely degrade the performance of the UAV localization estimates. In this paper we propose another innovative alternative, which is based on the Hinfin nonlinear filtering to avoid issues linked with classical filtering techniques and getting a significant robustness. This filtering approach is based on the Hinfin robust control theory, results, comparison with the EKF filter and validation on a simulation of a 3D flight scenario are presented.