Among the methods of the multi-source navigation filter, as a distributed method, the federated filter has a small calculation amount with Gaussian state noise, and it is easy to achieve global optimization. However, when the state noise is time-varying or its initial estimation is not accurate, there will be a big difference with the true value in the result of the federated filter. For the systems with time-varying noise, adaptive filter is widely used for its remarkable advantages. Therefore, this paper proposes a federated Sage–Husa adaptive filter for multi-source navigation systems with time-varying or mis-estimated state noise. Because both the federated and the adaptive principles are different in updating the covariance of the state noise, it is required to weight the two updating methods to obtain a combined method with stability and adaptability. In addition, according to the characteristics of the system, the weighting coefficient is formed by the exponential function. This federated adaptive filter is applied to the SINS/CNS/GNSS integrated navigation, and the simulation results show that this method is effective.

As a new information provider of autonomous navigation, the on-orbit landmark observation offers a new means to improve the accuracy of autonomous positioning and attitude determination. A novel autonomous navigation method based on the landmark observation and the inertial system is designed to achieve the high-accuracy estimation of the missile platform state. In the proposed method, the navigation scheme is constructed first. The implicit observation equation about the deviation of the inertial system output is derived and the Kalman filter is applied to estimate the missile platform state. Moreover, the physical observability of the landmark and the mathematical observability of the navigation system are analyzed. Finally, advantages of the proposed autonomous navigation method are demonstrated through simulations compared with the traditional celestial-inertial navigation system and the deeply integrated celestial-inertial navigation system.

Global Navigation Satellite System (GNSS) can be applied for the navigation of the high-orbit satellites. The system observability will change due to the changes in the visible satellite numbers and the spatial geometry between the navigation satellites and the users in the navigation system. The influence of the observability changing is not considered in the traditional navigation filter algorithm. In this paper, an optimized navigation filter method based on observability analysis is proposed. Firstly, a novel criterion for the relative observable degree is proposed for each observation component by making use of observation data from previous and posterior time simultaneously. Secondly, according to the relationship between observability and navigation filter accuracy, a novel optimized navigation filter method is constructed by introducing an adjusting factor based on the relative observable degree. Through the comparative simulations with the traditional Extended Kalman Filter (EKF), the optimized navigation filter method can reduce the estimation error of position and velocity by about 36% and 44% respectively. Therefore, the superiority of the proposed filter optimization algorithm is verified.

Based on the situation that the traditional SINS (strapdown inertial navigation system)/CNS (celestial navigation system) integrated navigation system fails to realize all-day and all-weather navigation, this paper proposes a SINS/Landmark integrated navigation method based on landmark attitude determination to solve this problem. This integrated navigation system takes SINS as the basic scheme and uses landmark navigation to correct the error of SINS. The way of the attitude determination is to use the landmark information photographed by the landmark camera to complete feature matching. The principle of the landmark navigation and the process of attitude determination are discussed, and the feasibility of landmark attitude determination is analyzed, including the orthogonality of the attitude transform matrix, as well as the influences of the factors such as quantity and geometric position of landmarks. On this basis, the paper constructs the equations of the SINS/Landmark integrated navigation system, testifies the effectiveness of landmark attitude determination on the integrated navigation by Kalman filter, and improves the navigation precision of the system.

Satellite condition monitoring based on telemetry data is critical for early fault pblackiction and proactive failure prevention. Distinct from conventional monitoring scenarios, satellite telemetry data typically lack reliable labels, and training datasets often contain unlabeled anomalous samples. These issues can introduce unexpected biases into reconstruction models, severely compromising their reliability and practicality. To address these challenges, this paper proposes an adaptive robust reconstruction strategy integrated into a hybrid modeling framework combining long short-term memory and self-attention mechanisms. Specifically, an M-estimator is incorporated with a novel custom-designed weighting function; this function is twice differentiable, ensuring the continuity of gradient-based optimization while adaptively and rapidly blackucing the weights of abnormal samples, thereby suppressing the interference of abnormal samples. Furthermore, by integrating a state space model, the variational Bayesian method is employed to formulate the global loss function, which simultaneously captures spatiotemporal dependencies inherent in the time series data and enforces regularization constraints on anomalous samples. Dedicated algorithms for model training and hyperparameter adaptive tuning are also developed to guarantee the model’s stability and reproducibility. Extensive condition monitoring experiments are conducted on real-world satellite telemetry datasets. The results demonstrate that the proposed method outperforms traditional approaches in nearly all detection performance metrics, and achieves both lower false negative and false alarm rates, even when the training data contains unlabeled anomalous samples.