Search

Current cooperative positioning schemes are complex, and their performances vary, urgently necessitating evaluation. In view of the specific characteristics of cooperative positioning systems (CPSs) in terms of physical data, decision-making factors, and the professionalism of decisions, this study aims to construct a reasonable multicriteria cooperative positioning decision-making (MCCPDM) evaluation framework. The study applies generative adversarial networks (GANs) to the decision-making process, proposes a weighted fusion of Pythagorean fuzzy sets (PFS) and hesitant fuzzy sets (HFS), and provides a method for determining the weights of decision-making factors that combines objective physical entropy information with subjective multilevel judgments. An MCCPDE method integrating technique for order preference by similarity to ideal solution (TOPSIS) is developed under the weighted Pythagorean hesitant fuzzy environment. Finally, through experiments evaluating cooperative positioning schemes, the proposed framework is proved to be highly effective and practical in cooperative positioning evaluation issues, and the method presented in this article surpasses the best baseline method by two orders of magnitude in terms of optimal ranking proximity.

With the development of vehicle applications such as intelligent transportation and autonomous driving, the application fields based on location services have increasingly higher requirements for vehicle positioning reliability and real-time accuracy. However, the existing single navigation source of vehicles makes it difficult to realize real-time and high-precision positioning in different scenarios. The current multi-source information fusion methods have the problems of low generalization ability, poor expansibility, and high computational complexity, so it is challenging to apply in the field of vehicle positioning. To solve the above problems, this paper proposes a vehicle heterogeneous multi-source information fusion positioning method (MIFP) based on information probability, which converts the multiple heterogeneous navigation sources into information probability models to realize the unification of the time-frequency parameter format and designs an information fusion algorithm to realize the rapid fusion based on the theory of relative entropy. Through simulation tests and experimental verification by comparing with mainstream information fusion methods, such as the UKF method, the FGA method, and the NNA method, the MIFP method has high positioning accuracy and strong real-time performance. It can effectively solve the problems of weak expansion ability and large calculation amounts of current vehicle fusion positioning models. In the case of interference or mutation, the MIFP method can also suppress the influence of sudden errors on vehicle positioning.

Owing to the flexible and lightweight characteristics, unmanned aerial vehicles (UAVs) are widely used in the field of cooperative navigation and positioning technology. Furthermore, wireless sensor networks (WSNs) provide a bridge for information exchange and fusion between the cooperative UAV nodes. Focusing on the demand of the lightweight and real-time dynamic requirements in the UAV-WSN, this article proposes a cooperative positioning algorithm based on manifold gradient filtering assisted by geometric dilution of precision (GDOP). In this algorithm, the measurement model between the cooperative sensors nodes in UAV-WSN is used to construct the Riemannian manifold. By deriving the gradient on the manifold, the fastest descent direction in iteration can be determined. The GDOP corresponding to the geometric configuration of the cooperative UAV nodes is derived, which is applied to modifying the iterative descent rate and making the algorithm converge quickly. The simulation results show that the algorithm converges fast and has good performance in accuracy. Moreover, the algorithm also exhibits a certain level of robustness in extremely harsh environments.