%0 Journal Article
%T 毫米波雷达通信一体化大规模阵列天线混合波束形成<br>Hybrid Beamforming of Millimeter Wave Radar Communication Integrated with Large Scale Array Antenna
%A 余苗
%A 余小游
%A 曾胜艳
%A 杨琦
%J Journal of Antennas
%P 14-19
%@ 2325-2243
%D 2021
%I Hans Publishing
%R 10.12677/JA.2021.102003
%X 本文研究毫米波车载雷达通信一体化系统中大规模阵列天线混合波束形成的优化设计问题。考虑的设计自由度包括源车辆发射端处的混合波束形成器、源车辆接收端处的雷达接收滤波器和接收车辆接收端处的通信基带合成器。本文提出一种最小化均方误差(MMSE)准则下的混合波束形成优化设计方法，在兼顾发射端模拟波束形成器单位模约束、发射端发射功率、通信数字波束、雷达最佳波束、雷达接收端信干噪比(SINR)等约束条件的前提下，将涉及多约束变量的非凸优化问题分解为四个子问题后，采用交替迭代优化机制(AIOM)求解混合波束形成问题。仿真实验表明，AIOM求解得到的大规模阵列天线混合波束形成，既有良好的收敛性，又有良好的通信与雷达折衷性能。<br />
This paper studies the design of hybrid beamforming (HBF) for large-scale array antenna in milli-meter wave vehicle radar and communication integration system. The design freedom includes the HBF at the transmitter of the source vehicle, the radar filter at the receiver of the source vehicle and the communication baseband combiner at the receiver of the recipient vehicle. In this paper, a HBF optimization design method based on the minimum mean square error (MMSE) criterion is pro-posed, which takes into account the unit mode constraint of the transmitter analog beamformer, the transmit power constraint, the communication digital beampattern, the optimal radar beampattern and the signal to interference noise ratio (SINR) of the radar receiver. After decomposing the non-convex optimization problem with multiple variables into four subproblems, the alternating iterative optimization mechanism (AIOM) is used to solve the optimization problem. Simulation re-sults show that the HBF of large-scale array antenna obtained by AIOM has good convergence and good tradeoff performance between communication and radar.
%K 毫米波，大规模阵列天线，混合波束形成，雷达通信一体化，交替迭代优化机制<br>Millimeter Wave
%K Large-Scale Array Antennas
%K Hybrid Beamforming
%K Radar and Communication Integration
%K Alternating Iterative Optimization Mechanism
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=43427