The 5G cellular network is expected to provide core service platform for the expanded Internet of Things (IoT) by supporting enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable low latency communications (URLLC). Unmanned aerial vehicles (UAVs), also known as drones, provide civil, commercial, and government services in various fields. Particularly in a 5G IoT scenario, UAV-aided network communications will fulfill an increasingly important role and will require the tracking of multiple UAV targets. As UAVs move quickly, maintaining the stability of the communication connection in 5G will be a challenge. Therefore, it is necessary to track the trajectory of UAVs. At present, the GM-PHD filter has a problem that the new target intensity must be known, and it cannot obtain the moving target trajectory and the influence of the clutter is likely to cause false alarm. A UAV-PHD filter is proposed in this work to improve the traditional GM-PHD filter by applying machine learning to the emergency detection and trajectory tracking of UAV targets. An out-of-sight detection algorithm for multiple UAVs is then presented to improve tracking performance. The method is assessed by simulation using MATLAB, and OSPA distance is utilized as an evaluation indicator. The simulation results illustrate that the proposed method can be applied to the tracking of multiple UAV targets in future 5G-IoT scenarios, and the performance is superior to the traditional GM-PHD filter.
Space information network (SIN) plays an extremely important role in civil and military applications. However, a specific definition of SIN still remains a challenging issue. Unlike space communication network (SCN), which merely focuses on information delivery, SIN has the ability to process the delivered information and providing corresponding services based on the information, which makes SIN seem to be able to understand information. This paper offers architecture and corresponding network model for timespace uninterrupted SIN. Initially, both physical and logical constitution of SIN architecture are presented with several corresponding constellation designs, which is the fundamental work for setting nodes in SIN. Based on this SIN architecture, a hierarchical autonomous system (AS)-based network model for SIN is proposed in order to manage SIN more efficiently by separating the whole network into several relatively stable ASs. Furthermore, we analyze topology control schemes and network capacity trend of AS-model based SIN. This paper gives the future research directions in the conclusion. INDEX TERMS Constellation design, hierarchical autonomous system, network capacity, space information network, topology control.
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