Due to the functional limitations of a single UAV, UAV clusters have become an important part of smart cities, and the relative positioning between UAVs is the core difficulty in UAV cluster applications. Existing UAVs can be equipped with satellite navigation, radio navigation, and other positioning equipment, but in complex environments, such as urban canyons, various navigation sources cannot achieve full positioning information due to occlusion, interference, and other factors, and existing positioning fusion methods cannot meet the requirements of these environments. Therefore, demand exists for the real-time positioning of UAV clusters. Aiming to solve the above problems, this paper proposes multisource fusion UAV cluster cooperative positioning using information geometry (UCP-IG), which converts various types of navigation source information into information geometric probability models and reduces the impact of accidental errors, and proposes the Kullback–Leibler divergence minimization (KLM) fusion method to achieve rapid fusion on geometric manifolds and creatively solve the problem of difficult fusion caused by different positioning information formats and parameters. The method proposed in this paper is compared with the main synergistic methods, such as LS and neural networks, in an ideal scenario, a mutation error scenario, and a random motion scenario. The simulation results show that by using UAV cluster movement, the method proposed in this paper can effectively suppress mutation errors and achieve fast positioning.

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 timefrequency 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.

The development of smart cities calls for improved accuracy in navigation and positioning services; due to the effects of satellite orbit error, ionospheric error, poor quality of navigation signals and so on, it is difficult for existing navigation technology to achieve further improvements in positioning accuracy. Distributed cooperative positioning technology can further improve the accuracy of navigation and positioning with existing GNSS (Global Navigation Satellite System) systems. However, the measured range error and the positioning error of the cooperative nodes exhibit larger reductions in positioning accuracy. In response to this question, this paper proposed a factor graph-aided distributed cooperative positioning algorithm. It establishes the confidence function of factor graphs theory with the ranging error and the positioning error of the coordinated nodes and then fuses the positioning information of the coordinated nodes by the confidence function. It can avoid the influence of positioning error and ranging error and improve the positioning accuracy of cooperative nodes. In the simulation part, the proposed algorithm is compared with a mainly coordinated positioning algorithm from four aspects: the measured range error, positioning error, convergence speed, and mutation error. The simulation results show that the proposed algorithm leads to a 30–60% improvement in positioning accuracy compared with other algorithms under the same measured range error and positioning error. The convergence rate and mutation error elimination times are only $1/5$ to $1/3$ of the other algorithms.

With the rapid development of large urban agglomerations and the increasing complexity of urban roads, the high-precision positioning of vehicles has become the cornerstone for the application of vehicle core technologies such as automatic driving. The real-time positioning accuracy of satellite navigation is easily affected by urban canyons, and its stability is poor; thus, how to use the information of the internet of vehicles to achieve satellite navigation fusion has become a difficult problem of multivehicle cooperative positioning. Aiming at this problem, this paper proposes a multivehicle 3D cooperative positioning algorithm based on information geometric probability fusion of GNSS/wireless station navigation (MVCP-GW), which creatively converts various navigation source information into an information geometric probability model, unifies navigation information time–frequency parameters, and reduces the impact of sudden error. Combined with the Kullback–Leibler algorithm (KLA) fusion method, it breaks off the shackles of the probabilistic two-dimensional model and achieves multivehicle three-dimensional cooperative positioning. Compared with the existing cooperative positioning algorithms in the performance of accuracy stability, applicability, obstruction scenarios, and physical verification, the simulation results and physical verification show that the MVCP-GW algorithm can effectively improve real-time vehicle positioning and the stability of vehicle positioning, as well as resist the impact of obstructed environments.

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 (WSN) 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 paper 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 utilized 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.