Topology tracking of dynamic UAV wireless networks

作     者：Yehui SONG Guoru DING Jiachen SUN Jinghua LI Yitao XU Yehui SONG;Guoru DING;Jiachen SUN;Jinghua LI;Yitao XU

作者机构：College of Communications EngineeringArmy Engineering University of PLANanjing 210007China 

出 版 物：《Chinese Journal of Aeronautics》 (中国航空学报（英文版）)

年 卷 期：2022年第35卷第11期

页      面：322-335页

核心收录：

学科分类：0810[工学-信息与通信工程] 08[工学] 082503[工学-航空宇航制造工程] 0714[理学-统计学（可授理学、经济学学位）] 0825[工学-航空宇航科学与技术] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(Nos.U20B2038,61871398,61901520 and 61931011) the Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province,China(No.BK20190030)。 

主　　题：Dynamic topology inference Event detection Hawkes process UAV swarm Wireless networks 

摘      要：Wireless network is the communication foundation that supports the intelligentization of Unmanned Aerial Vehicle(UAV) swarm. The topology of UAV communication network is the key to understanding and analyzing the behavior of UAV swarm, thus supporting the further prediction of UAV operations. However, the UAV swarm network topology varies over time due to the high mobility and diversified mission requirements of UAVs. Therefore, it is important but challenging to research dynamic topology inference for tracking the topology changes of the UAV network,especially in non-cooperative manner. In this paper, we study the problem of inferring UAV swarm network topology based on external observations, and propose a dynamic topology inference method. First, we establish a sensing framework for acquiring the communication behavior of the target network over time. Then, we expand the multi-dimensional dynamic Hawkes process to model the communication event sequence in a dynamic wireless network. Finally, combining the sliding time window mechanism, the maximum weighted likelihood estimation is applied to inferring the network topology. Extensive simulation results demonstrate the effectiveness of the proposed method.