摘要
级联毫米波雷达将多个射频芯片进行级联,可实现大规模多输入多输出(multiple-input multiple-output,MIMO)天线阵列。然而,现有毫米波雷达的测角方法存在精确度不足、旁瓣过高等问题。对此,提出一种基于稀疏矩阵填充的级联毫米波雷达高分辨测角方法。首先,建立级联毫米波雷达二维稀疏阵列的阵列信号模型。然后,将该阵列的单快拍信号构造为低秩汉克尔(Hankel)矩阵,使用加权非精确增广拉格朗日乘子法进行稀疏矩阵填充,并将填充后的矩阵转化为均匀虚拟阵列的单快拍信号。最后,使用常规波束形成(conventional beamforming,CBF)方法估计目标角度信息并进行配对。仿真和实测实验结果表明,所提方法能够精确地估计目标角度信息,并有效抑制峰值旁瓣电平。
Cascaded millimeter wave radar cascades multiple radio frequency chips to realize large-scale multiple-input multiple-output(MIMO)antenna array.However,the existing angle measurement methods of millimeter wave radar have problems such as insufficient accuracy and high sidelobes.To solve these problems,a high resolution angle measurement method for cascaded millimeter wave radar is proposed based on sparse matrix completion.Firstly,a two-dimensional sparse array signal model of the cascaded millimeter wave radar is established.Then,the single snapshot signal of the array is converted into a low-rank Hankel matrix,the weighted inexact augmented Lagrange multiplier method is used for sparse matrix completion,and the completed matrix is converted into the single snapshot signal of uniform virtual array.Finally,conventional beamforming(CBF)method is used to estimate the angles of the targets and the angles of the targets are paired.The simulation and experimental results show that the proposed method can accurately estimate the angles of the targets and effectively suppress peak sidelobe level.
作者
张明龙
吴雨林
魏文强
沈园杰
郭世盛
崔国龙
ZHANG Minglong;WU Yulin;WEI Wenqiang;SHEN Yuanjie;GUO Shisheng;CUI Guolong(School of Information and Communication Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China;Yangtze Delta Region Institute(Quzhou),University of Electronic Science and Technology of China,Quzhou 324003,China)
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2024年第8期2629-2640,共12页
Systems Engineering and Electronics
基金
衢州市财政资助科研项目(2022D008,2022D005)
广东省重点领域研发计划(2020B090905002)资助课题。
关键词
级联毫米波雷达
波达方向估计
稀疏矩阵
矩阵填充
cascaded millimeter wave radar
direction of arrival(DOA)estimation
sparse matrix
matrix completion
