The Kalman filter (KF) is a form of optimal estimator characterized by recursive evaluation, which has been widely applied to the navigation sensor fusion. Utilizing the KF requires that all the plant dynamics and noise processes are completely known, and the noise process is zero mean white noise. If the theoretical behaviour of the filter and its actual behaviour do not agree, divergence problems tend to occur. The adaptive algorithm has been one of the approaches to prevent divergence problems in the Kalman filter when precise knowledge on the system models is not available. Two popular types of adaptive Kalman filter are the innovation-based adaptive estimation (IAE) approach and the adaptive fading Kalman filter (AFKF) approach. In this paper, an approach involving the concept of the two methods is presented. The proposed method is a synergy of the IAE and AFKF approaches. The ratio of the actual innovation covariance based on the sampled sequence to the theoretical innovation covariance will be employed for dynamically tuning two filter parameters – fading factors and measurement noise scaling factors. The method has the merits of good computational efficiency and numerical stability. The matrices in the KF loop are able to remain positive definitive. Navigation sensor fusion using the proposed scheme will be demonstrated. Performance of the proposed scheme on the loosely coupled GPS/INS navigation applications will be discussed.