基于目标检测的混合分布式PA-MIMO雷达系统阵元优化部署

齐铖 谢军伟 张浩为 丁梓航 杨潇

齐铖, 谢军伟, 张浩为, 等. 基于目标检测的混合分布式PA-MIMO雷达系统阵元优化部署[J]. 雷达学报, 2023, 12(3): 576–589. doi: 10.12000/JR22159
引用本文: 齐铖, 谢军伟, 张浩为, 等. 基于目标检测的混合分布式PA-MIMO雷达系统阵元优化部署[J]. 雷达学报, 2023, 12(3): 576–589. doi: 10.12000/JR22159
QI Cheng, XIE Junwei, ZHANG Haowei, et al. Element configuration optimization of hybrid distributed PA-MIMO radar system based on target detection[J]. Journal of Radars, 2023, 12(3): 576–589. doi: 10.12000/JR22159
Citation: QI Cheng, XIE Junwei, ZHANG Haowei, et al. Element configuration optimization of hybrid distributed PA-MIMO radar system based on target detection[J]. Journal of Radars, 2023, 12(3): 576–589. doi: 10.12000/JR22159

基于目标检测的混合分布式PA-MIMO雷达系统阵元优化部署

DOI: 10.12000/JR22159
基金项目: 国家自然科学基金(62001506)
详细信息
    作者简介:

    齐 铖,博士生,主要研究方向为MIMO雷达检测、雷达资源调度

    谢军伟,博士,教授,主要研究方向为雷达干扰与抗干扰技术、新体制雷达系统等

    张浩为,博士,副教授,主要研究方向为雷达资源管理相关技术、认知雷达理论等

    丁梓航,博士生,主要研究方向为频控阵雷达相关技术、认知雷达理论等

    杨 潇,博士生,主要研究方向为毫米波集成电路,微波/毫米波前端组件设计等

    通讯作者:

    齐铖 qc_afeu@163.com

    张浩为 zhw_xhzf@163.com

  • 责任主编:严俊坤 Corresponding Editor: YAN Junkun
  • 中图分类号: TN972

Element Configuration Optimization of Hybrid Distributed PA-MIMO Radar System Based on Target Detection

Funds: The National Natural Science Foundation of China (62001506)
More Information
  • 摘要: 该文建立混合分布式相控阵-多输入多输出(PA-MIMO)雷达系统模型,推导出基于Neyman-Pearson (NP)准则的似然比检测(LRT)器,在收发两端实施子阵级和阵元级优化部署,达到对雷达系统中相参增益和空间分集增益协调优化的目的。针对整数规划的子阵、阵元部署模型,提出基于量子粒子群优化的随机取整(SR-QPSO)求解算法,在较少的迭代步骤内获得最优阵元配置策略,实现子阵级和阵元级之间的联合优化。最后,通过对3个典型优化问题进行数值仿真,所提出的混合分布式PA-MIMO雷达系统优化配置较其他典型雷达系统有较大提升,探测概率达到0.98,有效距离达到1166.3 km,探测性能得到显著提升。

     

  • 图  1  混合分布式PA-MIMO雷达结构示意图

    Figure  1.  Schematic diagram of the hybrid distributed PA-MIMO radar structure

    图  2  混合分布式PA-MIMO雷达阵元优化配置结构图

    Figure  2.  Structure diagram of hybrid distributed PA-MIMO radar array element optimization configuration

    图  3  混合分布式PA-MIMO雷达信号处理流程

    Figure  3.  Signal processing flow of hybrid distributed PA-MIMO radar

    图  4  基于优化问题1的算法性能对比

    Figure  4.  Algorithm performance comparison based on optimization problem 1

    图  5  检测概率与接收端分集自由度的关系曲线

    Figure  5.  Relation curves between detection probability and diversity DOF at receiver

    图  6  检测概率和发射端分集自由度的关系曲线

    Figure  6.  Relation curves between detection probability and diversity DOF at transmitter

    图  7  不同虚警概率下检测概率与接收端分集自由度的关系曲线

    Figure  7.  Relation curves between detection probability and diversity DOF at receiver with different false alarm probabilities

    图  8  基于优化问题2的算法性能比较

    Figure  8.  Algorithm performance comparison based on optimization problem 2

    图  9  雷达有效作用距离和接收端分集自由度的关系曲线

    Figure  9.  Relation curves between radar effective range and diversity DOF at receiver

    图  10  雷达有效作用距离与发射端分集自由度的关系曲线

    Figure  10.  Relation curves between effective range of radar and diversity DOF at transmitter

    图  11  不同检测概率下雷达有效作用距离与接收端分集自由度的关系曲线

    Figure  11.  Relation curves between radar effective range and diversity DOF at receiver with different detection probabilities

    图  12  不同虚警概率下雷达有效作用距离与接收端分集自由度的关系曲线

    Figure  12.  Relation curves between effective range of radar and diversity DOF at receiver with different false alarm probabilities

    图  13  不同检测概率下雷达阵元配置策略

    Figure  13.  Radar array element configuration strategies with different detection probabilities

    图  14  不同虚警概率下雷达阵元配置策略

    Figure  14.  Radar array element configuration strategies with different false alarm probabilities

    表  1  基于量子粒子群优化的随机取整求解算法流程

    Table  1.   SR-QPSO algorithm solution flow

     (1) 初始化解空间,设定搜索维度D、种群规模W和最大迭代次
       数Q,缩扩张因子$ \alpha = 0.8 $;
     (2) 粒子均匀随机散布于$ {\text{[}} - P_{{\text{min}}}^{},P_{{\text{max}}}^{}{\text{]}} $,记粒子位置初始值$ P_{i,t}^j $;
     (3) 规格化各粒子的位置参数,按照其小数位作为随机概率取整;
     (4) 计算个体当前位置$ {P_{i,j}}{\text{(1)}} $与全局最优位置$ {P_{\text{g}}}{\text{(}}t{\text{)}} $;
     (5) 设定收缩因子的上下界${\alpha _0}$和${\alpha _1}$;
     (6) 计算量子行为粒子的吸引子$ p_{i,j}^{}{\text{(}}t{\text{)}} $;
     (7)   for $ t = 1 $ to Q do
     (8)     计算t 时刻迭代时的平均最好个体位置;
     (9)     for $ i = 1 $ to W do
     (10)       计算粒子并更新粒子位置$ P_{i,j}^{}{\text{(}}t{\text{)}} $,按照其小数
             位作为随机概率取整;
     (11)       若$ P_{i,j}^{}{\text{(}}t{\text{)}} \notin {\text{[}} - P_{{\text{min}}}^{},P_{{\text{max}}}^{}{\text{]}} $,设置$ P_{i,j}^{}{\text{(}}t + 1{\text{)}} $
             为最近的边界值;
     (12)     end for
     (13)   end for
    下载: 导出CSV

    表  2  各算法计算复杂度对比

    Table  2.   Algorithm computational complexity comparison

    算法计算复杂度
    SR-QPSO${\mathcal{O} }(QW)$
    SR-PSO${\mathcal{O} }(QW)$
    穷举搜索法$ > {\mathcal{O}}({2^{M + N}}) $
    下载: 导出CSV

    表  3  优化效果分析

    Table  3.   Optimization effect analysis

    项目最优指标值配置策略收敛时间
    优化问题1$ {P_{{\text{D}}\max }} = 0.98 $$ {\text{(}}\hat M,\hat N{\text{)}} = {\text{(}}1,13{\text{)}} $87.275 s
    优化问题2${R_{{\rm{E}}\max } } = 1166.3\;{\text{km} }$$ {\text{(}}\hat M,\hat N{\text{)}} = {\text{(}}1,5{\text{)}} $86.778 s
    优化问题3$ M,\hat M $取决于$ {P_{{\text{FA}}}} $, $ {P_{\text{D}}} $/
    MIMO雷达${P_{\text{D} } } = 0.63,\;{R_{\rm{E}}} = 1006\;{\text{km} }$$ {\text{(}}\hat M,\hat N{\text{)}} = {\text{(}}100,100{\text{)}} $/
    相控阵雷达${P_{\text{D} } } = 0.80,\;{R_{\rm{E}}} = 1000\;{\text{km} }$$ {\text{(}}\hat M,\hat N{\text{)}} = {\text{(}}1,1{\text{)}} $/
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-07-22
  • 修回日期:  2022-09-04
  • 网络出版日期:  2022-09-14
  • 刊出日期:  2023-06-28

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