In order to solve the problem of decreased navigation performance of the Global Navigation Satellite System (GNSS)/inertial navigation system (INS) integrated navigation systems in GNSS-denied environments, and to improve the navigation accuracy and robustness of the navigation system, a novel adaptive federated filter with a feedback scheme for a GNSS/INS/visual odometry (VO) integrated navigation system is proposed in this paper. A visual-inertial odometry system model with a multi-state constraint Kalman filter structure based on a polar geometry and trifocal tensor geometry between different images is established, which can provide better navigation accuracy in GNSS-denied environments. Moreover, a new method to obtain the information allocation factor according to the different navigation performances of local filters is deduced in this paper, which has low computational complexity and a simple structure. Meanwhile, an abnormal measurement detection algorithm based on fuzzy logic is proposed to detect the abnormal measurements of local filters. The results of the vehicle experiment with the publicly available real-world KITTI dataset show that the proposed algorithm can obtain reliable navigation results in GNSS-denied environments and improve the navigation accuracy and robustness of the GNSS/INS/VO integrated navigation system.

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Traditional Global Navigation Satellite Systems (GNSS) experience their limitations in urban canyons. However, it is significant to improve the accuracy of positioning with the rapid development of smart cities. To solve this problem, a UGV-UAV robust cooperative positioning algorithm with object detection is proposed, which utilises an unmanned aerial vehicle (UAV) to assist an unmanned ground vehicle (UGV) to achieve accurate positioning. When the UAV is in the sky with a good reception of satellite signals, the UGV uses the YOLOv3 object detection method to detect the UAV in images captured by camera, and acquires visual measurements including angles and ranges of the ground camera relative to the UAV through the proposed monocular vision measuring with object detection (ODMVM) model. Then, in order to solve the problem that visual measurement is disturbed by the real world, a robust Kalman filter is introduced that integrates measurements from available GNSS, inertial measurement unit (IMU), monocular camera, and the position broadcast of cooperative UAV to obtain more robust and accurate position estimation. Experimental and simulation results show that the proposed cooperation positioning algorithm can improve the positioning accuracy by 73.63% compared with the traditional cooperation positioning algorithm in urban canyons.

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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.

Vehicular ad hoc networks (VANET) are an emergent technology with a promising future. VANETs are quite different from mobile ad hoc networks (MANETs) in terms of characteristics, challenges, system architecture, and application. One of the significant challenges of the VANET is the security from two essential points of view, the prediction and prevention attackers. In this article, we proposed an operating system that combined both software-defined network (SDN) and selforganizing map (SOM) for the 5G-based VANET system. The proposed system will be a new combination of the SDN-and a self-organizing map (SOM)-based network solution to enhance security in the two dimensions, detecting and preventing attacks. This article analysis the vulnerability of the network performance with taken into account the distributed denial of service (DDoS) attacks. Then, the security of the proposed system has been analyzed and checked with existing of the DDoS. The simulation results presented in this article show that, under general conditions of networks, the proposed system can enhance the networks performance compared to the existing work.

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Positioning information is the cornerstone of a new generation of electronic information technology applications represented by the Internet of Things and smart city. However, due to various environmental electromagnetic interference, building shielding, and other factors, the positioning source can fail. Cooperative positioning technology can realize the sharing of positioning information and make up for the invalid positioning source. When one node in the cooperative positioning network has error, the positioning stability of all nodes in the whole cooperative network will be significantly reduced, but the positioning probability information technology can effectively reduce the impact of mutation error. Based on this idea, this paper proposes an information-geometry-assisted distributed algorithm for probabilistic cooperative fusion positioning (IG-CP) of navigation information. The position information of different types of navigation sources is utilized to establish a probability density model, which effectively reduces the influence of a single position error on the whole cooperative position network. Combined with the nonlinear fitting characteristics of the information geometric manifold, mapping and fusion of the ranging information between cooperative nodes on the geometric manifold surface are conducted to achieve cooperative positioning, which can effectively improve the stability of the positioning results. The proposed algorithm is simulated and analyzed in terms of the node positioning error, ranging error, convergence speed, and distribution of the cooperative positioning network. The simulation results show that our proposed cooperative positioning algorithm can effectively improve the positioning stability and display better positioning performance.