基于IUPF算法的三维无人机毫米波波束跟踪  

3D UAV millimeter-wave beam tracking based on IUPF algorithm

作　　者：张俊杰 仲伟志 张璐璐 王俊智 朱秋明[2] ZHANG Junjie;ZHONG Weizhi;ZHANG Lulu;WANG Junzhi;ZHU Qiuming(College of Astronautics,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China;College of Electronic and Information Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)

机构地区：[1]南京航空航天大学航天学院,江苏南京211106 [2]南京航空航天大学电子与信息工程学院,江苏南京211106

出　　处：《系统工程与电子技术》2023年第1期257-263,共7页Systems Engineering and Electronics

基　　金：国家自然科学基金重大仪器研制项目(61827801);中央高校基本科研业务费(NS2020063)资助课题。

摘　　要：由于无人机毫米波通信技术具有高速数据传输和广域网络覆盖能力,因此在军用和民用领域中拥有广阔的应用前景。针对无人机毫米波通信需要进行精确的波束跟踪这一问题,提出一种基于改进无迹卡尔曼粒子滤波算法的三维波束跟踪方法。该方法首先利用无迹卡尔曼滤波建立建议密度函数并更新采样粒子;然后计算每一个采样粒子的权值,并在归一化后再次对粒子进行重采样;最后计算粒子均值,得到波束跟踪角度。仿真结果表明,该方法相较于以往毫米波波束跟踪方法大大降低了估计误差,显著提高了波束的跟踪精度。Unmanned aerial vehicle millimeter-wave communication technology has broad application prospects in military and civilian fields due to its high-speed data transmission and wide-area network coverage capabilities.Aiming at the problem that the unmanned aerial vehicle mmWave communication needs accurate beam tracking,a three-dimensional beam tracking method based on the improved unscented Kalman particle filter algorithm is proposed.This method,firstly,uses the unscented Kalman filter to establish the proposed density function and updates the sampled particles;then calculates the weight of each sampled particle,and re-samples the particles after normalization;finally calculates the particle average value to obtain the beam tracking angle.The simulation results show that compared with the previous millimeter-wave beam tracking methods,this method greatly lowers the estimation error,and significantly improves the beam tracking accuracy.

关 键 词：无人机通信 毫米波 波束跟踪 改进无迹卡尔曼粒子滤波 

分 类 号：TN928[电子电信—通信与信息系统]