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To improve the reliability and accuracy of the global positioning system (GPS)/micro electromechanical system (MEMS)-inertial navigation system (INS) integrated navigation system, this paper proposes two different methods. Based on wavelet threshold denoising and functional coefficient autoregressive (FAR) modeling, a combined data processing method is presented for MEMS inertial sensor, and GPS attitude information is also introduced to improve the estimation accuracy of MEMS inertial sensor errors. Then the positioning accuracy during GPS signal short outage is enhanced. To improve the positioning accuracy when a GPS signal is blocked for long time and solve the problem of the traditional adaptive neuro-fuzzy inference system (ANFIS) method with poor dynamic adaptation and large calculation amount, a self-constructive ANFIS (SCANFIS) combined with the extended Kalman filter (EKF) is proposed for MEMS-INS errors modeling and predicting. Experimental road test results validate the efficiency of the proposed methods.