As one of the information industry’s future development directions, the Internet of Things (IoT) has been widely used. In order to reduce the pressure on the network caused by the long distance between the processing platform and the terminal, edge computing provides a new paradigm for IoT applications. In many scenarios, the IoT devices are distributed in remote areas or extreme terrain and cannot be accessed directly through the terrestrial network, and data transmission can only be achieved via satellite. However, traditional satellites are highly customized, and on-board resources are designed for specific applications rather than universal computing. Therefore, we propose to transform the traditional satellite into a space edge computing node. It can dynamically load software in orbit, flexibly share on-board resources, and provide services coordinated with the cloud. The corresponding hardware structure and software architecture of the satellite is presented. Through the modeling analysis and simulation experiments of the application scenarios, the results show that the space edge computing system takes less time and consumes less energy than the traditional satellite constellation. The quality of service is mainly related to the number of satellites, satellite performance, and task offloading strategy.

In intelligent transportation system (ITS) applications, vehicles require robust and accurate positioning where global navigation satellite system signals are not available. However, despite extensive research, vehicle positioning with lane-level accuracy in a confined tunnel environment remains a challenging task. In this study, a positioning system based on a synchronized pseudolite system is proposed to enable low-latency and high-accuracy vehicle localization in tunnels. The pseudolites with cascading synchronization are deployed one-dimensionally. The effect of the cascading synchronization is analyzed; and a base station selection strategy is proposed to solve this problem. Fusion mechanization of the vehicle positioning system, digital elevation model, and laser ranging are adopted to provide three-dimensional positioning. Furthermore, deep integration is implemented using a low-cost inertial measurement unit to improve positioning accuracy and stability. The proposed localization solution is experimentally verified using a 4.6 km highway tunnel environment. The results of seven repeated experiments show that the positioning error is better than 11.55 cm.

In the positioning and navigation field, it is essential to use the direction-finding system to obtain the signal direction of arrival (DOA) and target position. The amplitude comparison-based monopulse (ACM) DOA algorithm performs a few calculations, has a simple system structure, and is widely used. The traditional ACM DOA algorithm uses the first-order Taylor expansion to introduce the nonlinear errors, and the angle measurement range is limited. In response to this problem, this study establishes a neural network model for error compensation, and it optimizes the traditional algorithm to obtain a better angle estimation performance. In order to perform an experiment with the proposed algorithm, a novel experimental device was designed. Two measurements at different angles were obtained by rotating the antenna. The ACM angle estimation used only one directional antenna. The results verified the optimization algorithm. The experimental results demonstrated that in comparison with the traditional first-order and the improved third-order Taylor expansion ACM DOA algorithm, the mean absolute error of this method reduced by 81.62% and 72.62%, respectively.