Unmanned aerial vehicle (UAV) swarm technology can expand the working scope of tasks and improve the overall task efficiency. Traditional single leader-follower fusion algorithms for cooperative navigation cannot fully utilize collaborative navigation information, and low reliability and navigation precision in interference cases. Meanwhile, the full connected fusion algorithm for cooperative navigation shows a large relative computing and communication burden and inflexibility to overcome the faults. Therefore, this paper proposes a collaborative navigation algorithm for a UAV swarm based on a hierarchical structure. A model for intervehicle collaborative measurement of UAV swarm cooperative navigation, with the line-of-sight calculation uncertainty estimation, is established. The hierarchical collaborative navigation fusion algorithm that could be adaptive to asynchronous updating of members in the swarm is designed. A comparison of simulation results of cooperative navigation of the UAV swarm by the different cooperative fusion algorithms is provided. The results indicate that the proposed method can improve the positioning accuracy and robustness of the cooperative navigation of a UAV swarm.

As navigation is a key to task execution of micro unmanned aerial vehicle (UAV) swarm, the cooperative navigation (CN) method that integrates relative measurements between UAVs has attracted widespread attention due to its performance advantages. In view of the precision and efficiency of cooperative navigation for low-cost micro UAV swarm, this paper proposes a sigma point belief propagation-based (SPBP) CN method that can integrate self-measurement data and inter-UAV ranging in a distributed manner so as to improve the absolute positioning performance of UAV swarm. The method divides the sigma point filter into two steps: the first is to integrate local measurement data; the second is to approximate the belief of position based on the mean and covariance of the state, and pass message by augmentation, resampling and cooperative measurement update of the state to realize a low-complexity approximation to traditional message passing method. The simulation results and outdoor flight test results show that with similar performance, the proposed CN method has a calculation load more than 20 times less than traditional BP algorithms.

Aerospace vehicle navigation systems are equipped with multi-source redundant navigation sensors. According to the characteristics of the above navigation system configuration, building a resilient navigation framework to improve the accuracy and robustness of the navigation system has become an urgent problem to be solved. In the existing integrated navigation methods, redundant information is only used for backup. So, it cannot use the redundant navigation information to improve the accuracy of the navigation system. In this paper, a resilient multi-source fusion integrated navigation method based on comprehensive information evaluation has been proposed by combining of qualitative analysis and quantitative analysis in information theory. Firstly, this paper proposes a multi-layer evaluation framework of redundant information and carries out quantitative analysis of redundant information with the information disorder analysis theory to improve the reliability of the navigation system. Secondly, a navigation output effectiveness evaluation system has been established to analyze the output of heterogeneous navigation subsystems qualitatively to improve the fusion accuracy. Finally, through the mutual correction of multi-level information evaluation results, the error decoupling between the output parameters of heterogeneous navigation sensors has been realized to improve the robustness of the system. The experimental results show that the method proposed in this paper can adaptively allocate and adjust the weight of navigation information at all levels, realize the “non-stop” work of the navigation system and enhance the resilient of the navigation architecture. The navigation accuracy is improved compared with the existing multi-source fusion algorithm, which reflects the reliability and robustness of this algorithm.

The collaborative exchange of information between members of a cluster can provide additional observations to improve the reliability of navigation. To address the problems of poor geometric configurations of satellites and low positioning accuracy of satellite navigation systems in complex environments, this paper proposes a collaboration‐augmented navigation method with optimisation of the geometric dilution of precision for a clustered aircraft. A collaboration‐augmented navigation model is established in which the differences between the characteristics of satellite pseudorange measurements and collaborative information measurements are taken into consideration to overcome heteroscedasticity. Then, an optimal collaboration‐augmented navigation algorithm is proposed on the basis of geometric configuration analysis. In this method, all visible satellites are projected onto the current horizontal plane, and in accordance with the satellite distribution, a satellite reference ring is constructed to determine the optimal collaborative area for an aircraft. Then, a collaborative aircraft selection is made to select an optimal aircraft to assist the satellite navigation system for configuration optimisation. Finally, the algorithm performance is simulated and verified by simulation, and the influence of the equivalent ranging error on the effectiveness of geometric configuration optimisation is analysed, indicating the effectiveness of the proposed method.

As to an aerospace vehicle, the flight span is large and the flight environment is complex. More than that, the existing navigation algorithms cannot meet the needs to provide accurate navigation parameters for aerospace vehicles, which results in the decline of navigation accuracy. This paper proposes a multi-layer, fault-tolerant robust filtering algorithm of aerospace vehicle in the launch inertial coordinate system to address this problem. Firstly, the launch inertial coordinate system is used as the reference coordinate system for navigation calculation, and the state equation and measurement equation of the navigation system are established in this coordinate system to improve the modeling accuracy of the navigation system. On this basis, a multi-layer, fault-tolerant robust filtering algorithm is designed to estimate and compensate the unknown input in the state equation in real time and adjust the noise variance matrix in the measurement equation adaptively. Simulation results show that the errors about the integrated navigation system output parameters are reduced, through this algorithm, which improves the attitude, velocity and position estimation accuracy of the integrated navigation system. In addition, the algorithm enhances the fault tolerance and robustness of the filtering algorithm.