The purpose of this research is to improve the robustness of the autonomous system in order to improve the position and velocity estimation of an Unmanned Aerial Vehicle (UAV). Therefore, new integrated SINS/GPS navigation scheme based on Interacting Multiple Nonlinear Fuzzy Adaptive H∞ Models (IMM-NFAH∞) filtering technique for UAV is presented. The proposed IMM-NFAH∞ strategy switches between two different Nonlinear Fuzzy Adaptive H∞ (NFAH∞) filters and each NFAH∞ filter is based on different fuzzy logic inference systems. The newly proposed technique takes into consideration the high order Taylor series terms and adapts the nonlinear H∞ filter based on different fuzzy inference systems via adaptive filter bounds (δi), along with disturbance attenuation parameter γ. Simulation analysis validates the performance of the proposed algorithm, and the comparison with nonlinear H∞ (NH∞) filter and that with different NFAH∞ filters demonstrate the effectiveness of UAV localization utilizing IMM-NFAH∞ filter. Keywords: Interacting multiple models, Integrated navigation system, Multi-mode estimation, Nonlinear fuzzy adaptive filter, Sensor data fusion, UAV localization