As one of the most critical issues for target track, $\alpha$ -jerk model is an effective maneuver target track model. Non-Gaussian noises always exist in the track process, which usually lead to inconsistency and divergence of the track filter. A novel Kalman filter is derived and applied on $\alpha$ -jerk tracking model to handle non-Gaussian noise. The weighted least square solution is presented and the standard Kalman filter is deduced firstly. A novel Kalman filter with the weighted least square based on the maximum correntropy criterion is deduced. The robustness of the maximum correntropy criterion is also analyzed with the influence function and compared with the Huber-based filter, and, moreover, the kernel size of Gaussian kernel plays an important role in the filter algorithm. A new adaptive kernel method is proposed in this paper to adjust the parameter in real time. Finally, simulation results indicate the validity and the efficiency of the proposed filter. The comparison study shows that the proposed filter can significantly reduce the noise influence for $\alpha$ -jerk model.

Strap-down inertial navigation system/celestial navigation system (SINS/CNS) integrated navigation is a high precision navigation technique for ballistic missiles. The traditional navigation method has a divergence in the position error. A deeply integrated mode for SINS/CNS navigation system is proposed to improve the navigation accuracy of ballistic missile. The deeply integrated navigation principle is described and the observability of the navigation system is analyzed. The nonlinearity, as well as the large outliers and the Gaussian mixture noises, often exists during the actual navigation process, leading to the divergence phenomenon of the navigation filter. The new nonlinear Kalman filter on the basis of the maximum correntropy theory and unscented transformation, named the maximum correntropy unscented Kalman filter, is deduced, and the computational complexity is analyzed. The unscented transformation is used for restricting the nonlinearity of the system equation, and the maximum correntropy theory is used to deal with the non-Gaussian noises. Finally, numerical simulation illustrates the superiority of the proposed filter compared with the traditional unscented Kalman filter. The comparison results show that the large outliers and the influence of non-Gaussian noises for SINS/CNS deeply integrated navigation is significantly reduced through the proposed filter.

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.

Accurate monocular pose estimation of spacecraft is essential for on-orbit servicing and proximity operations, yet this remains challenging because of large variations in the target scale and limited onboard computational resources. To address these challenges, this paper proposes SAPose, a lightweight geometry-guided pose-estimation framework that integrates multi-task keypoint prediction with geometric pose recovery. The proposed network jointly performs spacecraft detection and semantic keypoint localization using a stage-aware lightweight backbone and a scale-adaptive feature fusion structure, thereby enhancing geometric feature representation across different target scales. For distant spacecraft occupying only a small portion of the image, an adaptive coarse-to-fine inference strategy selectively activates ROI-based secondary refinement to recover weakened structural details while avoiding unnecessary computation for medium- and large-scale targets. In addition, confidence-ranked keypoint selection and object-space nonlinear refinement are employed to improve the stability and accuracy of pose recovery. Experiments on the Spacecraft Pose Estimation Dataset (SPEED) and the Spacecraft Keypoint Dataset (SKD) demonstrate that SAPose achieves competitive pose estimation accuracy with a compact model size and efficient inference, providing a practical balance between accuracy and computational cost for monocular spacecraft pose estimation.