Reference [22] defined a method which analyzes the observable degree according to the error attenuation degree of the initial state variables, but the method paid much attention to the initial error covariance and ignored the real-time performance of filter; besides, some studies proposed to use the Fisher information matrix are also used to measure the observability of the navigation system in some studies [23; 24; 25]. Currently, there are many navigation filter algorithms applied to the navigation systems of the high-orbit satellites, including traditional Kalman filter and various improved forms. In addition, many scholars have proposed a large number of corresponding improved filter algorithms for the nonlinearity and uncertainty of the actual navigation system as well as the non-gaussian and correlation of noise, etc. [26; 27; 28; 29; 30]. Reference [31] proposed an adaptive filter method based on the state observability. The method constructed some adaptive factors according to the proposed quantized observability of each state component. The filter gain attenuation method is simultaneously applied to adjust the components whose observability is weak. Reference [32] proposed an information fusion algorithm based on the observable degree. The algorithm determines the information distribution coefficient according to the observable degree of the observation model and takes the unscented filter as the federated filter algorithm. Reference [33] proposed a smart Kalman filter method based on the adaptive observability to solve the problem of inaccurate parameters of the observation equation. The method improves the filter robustness by iteratively solving the adaptive adjustment factor. However, the observable degree of the different state variables of the navigation system is inconsistent and the information transmitted to the subsequent filter estimation is also inconsistent. The previous research mainly used the observability directly as an adjustment factor and fed it back to the filter to improve the importance of the state variables with strong observability, while to reduce the influence of state variables with weak observability. Since the observability results obtained by different observability measurement methods are not consistent, it may cause the navigation filter divergence and reduce the overall navigation performance. In addition, the high-orbit satellite navigation is different from the general navigation system because the high-orbit satellite is higher than most orbits of the four major satellite navigation systems at present. Superadd the transmitting antenna of the GNSS satellite is facing the earth [34], and the angle between the main lobe signal transmission is limited. When the high-orbit satellite moves beyond the GNSS constellation, it can only receive the navigation satellite signal from the other side of the earth [35]. Therefore, the number of navigation satellites (i.e. the number of the visible satellites) that can be observed by the satellite at the receiver antenna position is limited. In a cycle of satellite operation, the number of visible navigation satellites changes constantly, which leads to the change of the observability. Therefore, it is of great significance to optimize the existing navigation filter algorithm and improve the overall performance of the navigation system of high-orbit satellites based on GNSS with the observability and the observable degree of the navigation system. Based on the above analysis, we propose an adaptive navigation filter optimization method based on the observability and the observable degree of local components. According to the attenuation degree between the error of the optimal solution obtained from the observation in a period and the error of the state variable estimated by the filter at the current time, a new observability calculation matrix is defined, and the relative observable degree of each state component of the system is obtained.