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Aiming at the problem that the extended Kalman filter (EKF) will diverge in combined navigation, a fuzzy control-based adaptive Kalman filter algorithm (FAKF) is studied, which calculates the exponential weighting factor through a fuzzy controller to adjust the size of the measurement noise in real time to ensure the filtering accuracy and effectively inhibit the dispersion problem of the filtering process. Aiming at the situation that GPS cannot provide the attitude angle for combined solution, the combination of polarized light and SINS is used for attitude solution. In this paper, the fuzzy control based adaptive Kalman filter algorithm (FAKF) and extended Kalman filter algorithm (EKF) are compared and verified by simulating the GPS/SINS/polarized light combined navigation system, which improves the accuracy by 56.81%, 65.17%, and 45.99% on the position of east, north, and heading, respectively, and 46.99% on the speed of east, north, and heading, 54.01% and 43.82% on east, north and skyward velocity, and 58.01% and 53.58% on pitch angle and heading angle, respectively, which proves the advantages of the method.