Aiming at the formation problem in the cooperative navigation of unmanned aerial vehicle swarm, a cooperative position error analysis method based on cooperative dilution of precision is studied in this article. During cooperative flight, the unmanned aerial vehicle swarm can use the received position and ranging information of the adjacent unmanned aerial vehicles to calculate the position, and fuse with its own sensor position information. The final positioning accuracy depends not only on the capability of the ranging sensor but also on the position accuracy and formation of the adjacent unmanned aerial vehicles. In this article, these influence factors are combined to put forward a cooperative dilution of precision calculation method suitable for unmanned aerial vehicle swarm cooperative navigation. On this basis, a cooperative integrated navigation method based on ranging information is designed. Finally, the performance of cooperative navigation of unmanned aerial vehicles in different formations is simulated and analyzed. The simulation result shows that the cooperative dilution of precision method proposed in this article can effectively analyze the influence of formation on the positioning accuracy of unmanned aerial vehicle swarm, and the final combined positioning result is consistent with the cooperative dilution of precision analysis result.

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.

Precise navigation is the key to guaranteeing the mission execution of unmanned aerial vehicle (UAV) swarms. Cooperative navigation realized through information interaction between UAVs can enhance the navigation performance of UAVs in complex environments. In this article, we construct a distributed cooperative navigation framework that can fuse the measurements from various onboard navigation sensors and inter-UAV ranging based on factor graph and belief propagation (BP). In view of the computational efficiency, we propose a simplified Gaussian particle filter for message passing and belief calculation, which reduces the computational load. The proposed algorithm is tested and verified using Monte Carlo simulations and flight test. The simulation results show that with similar positioning performance, the average processing time of the proposed algorithm is reduced by 88% compared to the SPAWN algorithm.

This paper focuses on the cooperative navigation of heterogeneous air-ground vehicle formations in a Global Navigation Satellite System (GNSS) challenged environment and proposes a cooperative navigation method based on motion estimation and a regionally optimal path planning strategy. In air-ground vehicle formations, unmanned ground vehicles (UGVs) are equipped with low-precision inertial navigation measurement units and wireless range sensors, which interact with unmanned aerial vehicles (UAVs) equipped with high-precision navigation equipment for cooperative measurement information and use the UAVs as aerial benchmarks for cooperative navigation. Firstly, the Interacting Multiple Model (IMM) algorithm is used to predict the next moment’s motion position of the UGVs. Then regional real-time path optimization algorithms are introduced to design the motion position of the high-precision UAVs so as to improve the formation’s configuration and reduce the geometric dilution of precision (GDOP) of the configuration. Simulation results show that the Dynamic Optimal Configuration Cooperative Navigation (DOC-CN) algorithm can reduce the GDOP of heterogeneous air-ground vehicle formations and effectively improve the overall navigation accuracy of the whole formation. The method is suitable for the cooperative navigation environment of heterogeneous air-ground vehicle formations under GNSS-challenged conditions.

In Internet of Things (IoT)-driven vehicular networks, accurate and reliable positioning services are critical for enabling vehicle collaboration and intelligence. The integration of Global Navigation Satellite Systems (GNSS) and Ultra-Wideband (UWB) technologies is widely employed for cooperative positioning (CP) of vehicles in IoT applications. However, in practical CP state estimation, GNSS and UWB measurements are often affected by environmental uncertainties such as non-line-of-sight (NLOS) conditions and multipath effects. This paper proposes a novel cooperative fault-tolerant positioning scheme based on Model-Mixed Robust Gaussian Belief Propagation (MMRGBP), designed for cooperative network positioning in interference environments. Firstly, we utilize belief propagation within a factor graph model for CP estimation, deriving the messages in the message passing (MP) process in Gaussian form. Subsequently, we introduce an improved Huber M estimation model under the Gaussian belief propagation framework, which approximates the non-Gaussian noise in the MP process as a Gaussian noise by dynamically adjusting the Huber function threshold to achieve more robust CP estimation. To address complex faulty measurements, we design an information interaction mechanism within the MP process, employing interactive multiple models for information fusion and switching the model of fault observation to prevent the introduction of serious positioning errors. Experimental results demonstrate that, compared to other advanced fault-tolerant CP methods such as RAIM-CPKF and Huber-CPKF filters, MMRGBP exhibits superior performance in both positioning accuracy and robustness. This makes MMRGBP particularly suitable for cooperative fault-tolerant positioning networks.

Based on house of quality concept, the relation between servitization strategy and strategic elements were confirmed through grey relational analysis method. Accordingly, the specific elements characteristics of manufacturing enterprise management core - talents & team and relationship, knowledge resources and flowing, information infrastructure and treatment methods, and organization structure and management were illustrated in three servitization stages - product servitization enlarging, servitization capability continuously improving and servitization integration and innovation. According to elements characteristics, the strategic targets for different servitization stages were brought forward, and four strategic elements specific managing methods were further offered for different servitization stages. The research can offer decision references for manufacturing enterprise to confirm element character and servitization stage, and give effective support to confirm management key points and methods of servitization in manufacturing enterprises at different stages.