基于多目标跟踪FANET网络的UAV协同通信算法研究  

Research on UAV Cooperative Communication Algorithm Based on Multi-Target Tracking FANET Network

作　　者：陆渊章[1,3] 谢海燕 吉训生 LU Yuanzhang;XIE Haiyan;JI Xunsheng(School of Electronic Information Engineering,Jiangsu Vocational and Technical College of Information Technology,Wuxi Jiangsu 214153,China;College of Applied Science and Technology,Illinois State University,Illinois 61790,USA;School of Internet of Things Engineering,Jiangnan University,Wuxi Jiangsu 214000,China)

机构地区：[1]江苏信息职业技术学院电子信息工程学院,江苏无锡214153 [2]伊利诺伊州立大学应用科学技术学院,美国伊利诺伊州61790 [3]江南大学物联网工程学院,江苏无锡214000

出　　处：《电子器件》2021年第6期1399-1403,共5页Chinese Journal of Electron Devices

基　　金：国家自然科学基金面上项目(61973140);江苏省专用集成电路设计重点实验室开放课题基金(2020KLOP006);江苏省高校优秀中青年教师和校长境外研修项目;江苏省高职院校教师专业带头人高端研修项目;2018年江苏省“333”工程第三层次培养对象资助项目(苏人才办[2018]6号)。

摘　　要：针对无人飞行器(unmanned aerial vehicles, UAV)群在飞行过程中动态无线通信飞行自组织网络(flying ad-hoc network, FANET)参数和扰动不确定的问题,采用多UAV无线协作通信模型研究动态不确定性,提出一种基于递归神经网络(recursive neural network, RNN)的多目标跟踪自适应控制算法。带有接入点控制方法的FANET网络可以优化无人机的通信系统容量和覆盖范围以及获得更快的响应速度。仿真结果表明:通过确定无人飞行器的最佳覆盖区域,所需总功率将有所降低,最大程度地确保地面设备最大的吞吐量,提高了UAV协作通信控制的准确性和灵敏度。Aiming at the problem of dynamic wireless communication flying ad-hoc network(FANET)parameters and disturbance uncertainty in the flight process of unmanned aerial vehicles(UAV),the dynamic uncertainty in the multi UAV wireless cooperative communication model is studied, and a multi-target tracking adaptive control algorithm based on recursive neural network(RNN) is proposed. The FANET network with access point control method can optimize the communication system capacity, coverage and faster response speed of UAV. The simulation results show that by determining the optimal coverage area of UAV,the total required power will be reduced, ensure the maximum throughput of ground equipment to the greatest extent, and improve the accuracy and sensitivity of UAV cooperative communication control.

关 键 词：无人飞行器 飞行自组织网络 基础架构网络 覆盖范围扩展 自适应控制 

分 类 号：TP306[自动化与计算机技术—计算机系统结构]