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.

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.

Fault identification for closed-loop control systems is a future trend in the field of fault diagnosis. Due to the inherent feedback adjustment mechanism, a closed-loop control system is generally very robust to external disturbances and internal noises. Closed-loop control systems often encourage faults to propagate inside the systems, which may lead to the consequence that faults amplitude becomes smaller and fault characteristics difference becomes more inapparent. Hence, it has been challenging to achieve fault identification for such systems. Traditional fault identification methods are not particularly designed for closed-loop control systems and thus cannot be applied directly. In this work, a new fault identification method is proposed, which is based on the deep neural network for closed-loop control systems. Firstly, the fault propagation mechanism in closed-loop control systems is theoretically derived, and the influence of fault propagation on system variables is analyzed. Then deep neural network is applied to find fault characteristics difference between different data modes, and a sliding window is used to amplify the fault-to-noise ratio and characteristics difference, with an aim to increase the identification performance. To verify this method, the simulations that are based on a numerical simulation model, the Tennessee industrial system and the satellite attitude control system are conducted. The results show that the proposed method is more feasible and more effective in fault identification for closed-loop control systems compared with traditional data-driven identification methods, including distance-based and angle-based identification methods.

Path planning poses a complex optimization challenge essential for the safe operation and successful mission execution of unmanned aerial vehicles (UAVs). Developing objectives, constraints, and decision-making processes for three-dimensional path planning involving multiple UAVs presents substantial challenges within the multi-objective optimization community. Traditional modeling approaches primarily aim to minimize path length and collision risks, often overlooking the need for a quantitative assessment of communication quality among UAVs. This neglect causes an inadequate representation of their true cooperative capabilities. In addition, there is difficulty in achieving an optimal balance between convergence, diversity, and feasibility. Therefore, this study introduces a bi-objective, three-dimensional path planning model specifically designed for UAVs. This model features an objective function that quantitatively evaluates inter-UAV communication quality throughout their flights. To solve this problem, this study proposes the dual-population regularity evolution algorithm (DPREA), which incorporates an auto-switching regularity evolutionary reproduction operator known as autoRE. It conducts extensive experiments across six testing suites and three path-planning simulation cases to validate the effectiveness of DPREA. The simulation results showed that its performance in addressing constrained multi-objective problems is significantly superior or at least comparable to that of state-of-the-art algorithms in most instances.

The observation error model of the underwater acoustic positioning system is an important factor to influence the positioning accuracy of the underwater target. For the position inconsistency error caused by considering the underwater target as a mass point, as well as the observation system error, the traditional error model best estimation trajectory (EMBET) with little observed data and too many parameters can lead to the ill-condition of the parameter model. In this paper, a multi-station fusion system error model based on the optimal polynomial constraint is constructed, and the corresponding observation system error identification based on improved spectral clustering is designed. Firstly, the reduced parameter unified modeling for the underwater target position parameters and the system error is achieved through the polynomial optimization. Then a multi-station non-oriented graph network is established, which can address the problem of the inaccurate identification for the system errors. Moreover, the similarity matrix of the spectral clustering is improved, and the iterative identification for the system errors based on the improved spectral clustering is proposed. Finally, the comprehensive measured data of long baseline lake test and sea test show that the proposed method can accurately identify the system errors, and moreover can improve the positioning accuracy for the underwater target positioning.

In angles-only initial relative orbit determination for non-cooperative targets, many existing methods require multiple angle measurements over a long time span or rely on iterative optimization. As a result, it is difficult to achieve fast initialization from a small number of measurements at the beginning of on-orbit navigation. To address this issue, this paper proposes a non-iterative initial relative orbit determination algorithm in a spherical coordinate framework. First, the relative motion equations are derived in detail in a spherical coordinate system centered on the line-of-sight direction, including the exact nonlinear two-body model and a linear time-invariant model applicable to near-circular orbits. Second, a non-iterative estimation algorithm that requires no prior state information is developed. The algorithm only needs to perform singular value decomposition on a single 6 × 6 matrix and solve a univariate polynomial. In the absence of camera offsets or orbital maneuvers, the initial relative state of the target can be obtained rapidly using only four angle measurements. Simulation results show that, compared with the classical iterative method, the proposed algorithm significantly improves computational efficiency while maintaining comparable or even superior estimation accuracy. It also exhibits excellent numerical stability and noise robustness, making it suitable for fast initialization in angles-only relative navigation of non-cooperative targets.