非理想检测下多雷达网络节点选择与辐射资源联合优化分配算法

时晨光 唐志诚 周建江 严俊坤 王子微

时晨光, 唐志诚, 周建江, 等. 非理想检测下多雷达网络节点选择与辐射资源联合优化分配算法[J]. 雷达学报(中英文), 2024, 13(3): 565–583. doi: 10.12000/JR23081
引用本文: 时晨光, 唐志诚, 周建江, 等. 非理想检测下多雷达网络节点选择与辐射资源联合优化分配算法[J]. 雷达学报(中英文), 2024, 13(3): 565–583. doi: 10.12000/JR23081
SHI Chenguang, TANG Zhicheng, ZHOU Jianjiang, et al. Joint collaborative radar selection and transmit resource allocation in multiple distributed radar networks with imperfect detection performance[J]. Journal of Radars, 2024, 13(3): 565–583. doi: 10.12000/JR23081
Citation: SHI Chenguang, TANG Zhicheng, ZHOU Jianjiang, et al. Joint collaborative radar selection and transmit resource allocation in multiple distributed radar networks with imperfect detection performance[J]. Journal of Radars, 2024, 13(3): 565–583. doi: 10.12000/JR23081

非理想检测下多雷达网络节点选择与辐射资源联合优化分配算法

doi: 10.12000/JR23081
基金项目: 国家自然科学基金(62271247),国防基础科研计划资助项目(JCKY2021210B004),南京航空航天大学前瞻布局科研专项资金,江淮前沿技术协同创新中心追梦基金课题资助(2023-ZM01D001)
详细信息
    作者简介:

    时晨光,博士,副教授,主要研究方向为飞行器射频隐身技术、网络化雷达资源管理等

    唐志诚,硕士生,主要研究方向为飞行器射频隐身技术

    周建江,博士,教授,主要研究方向为飞行器射频隐身技术、雷达目标特性分析、航空电子信息技术等

    严俊坤,博士,教授,主要研究方向为认知雷达、目标跟踪与定位、协同探测等

    王子微,博士,工程师,主要研究方向为雷达信号处理、目标跟踪等

    通讯作者:

    时晨光 scg_space@163.com

  • 责任主编:易伟 Corresponding Editor: YI Wei
  • 中图分类号: TN957

Joint Collaborative Radar Selection and Transmit Resource Allocation in Multiple Distributed Radar Networks with Imperfect Detection Performance

Funds: The National Natural Science Foundation of China (62271247), The Defense Industrial Technology Development Program (JCKY2021210B004), The Prospective Layout of Scientific Research Special Funds of Nanjing University of Aeronautics and Astronautics, Dreams Foundation of Jianghuai Advance Technology Center (2023-ZM01D001)
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  • 摘要: 该文针对分布式相控阵多雷达网络的多目标跟踪场景,研究非理想检测条件下的节点选择与辐射资源联合优化分配算法。首先,根据分布式相控阵多雷达网络构成、目标运动模型、雷达量测模型以及雷达节点检测情况,推导非理想检测下以雷达节点选择、辐射功率和信号带宽为变量的贝叶斯克拉默-拉奥下界(BCRLB)闭式解析表达式,并以此作为多目标跟踪精度衡量指标。在此基础上,以最小化系统各雷达节点对所有目标的总辐射功率为优化目标,以满足目标跟踪精度门限以及给定的系统射频辐射资源限制为约束条件,建立非理想检测条件下多雷达网络节点选择与辐射资源联合优化分配模型,对各时刻雷达节点选择、辐射功率和信号带宽等参数进行联合优化设计,以提升多雷达网络的射频隐身性能。最后,针对上述非线性、非凸优化问题,采用基于障碍函数法和循环最小化算法的4步分解算法进行求解。仿真结果表明,与现有算法相比,所提算法能在满足给定多目标跟踪精度的条件下有效降低分布式相控阵多雷达网络的总辐射功率,至少降低了约32.3%,从而提升其射频隐身性能。

     

  • 图  1  多雷达网络多目标跟踪工作步骤

    Figure  1.  The working steps for multitarget tracking of multiple radar networks

    图  2  基于本文所提算法的多雷达网络多目标跟踪闭环过程

    Figure  2.  The closed-loop process of proposed algorithm for multitarget tracking in multiple radar networks

    图  3  仿真场景1多雷达网络分布与目标运动轨迹

    Figure  3.  Deployment of multiple radar networks and trajectories of moving targets in scenario 1

    图  4  仿真场景1目标1的雷达节点选择与辐射资源优化分配结果

    Figure  4.  Radar node selection and transmit resource optimization results of target 1 in scenario 1

    图  5  仿真场景1目标2的雷达节点选择与辐射资源优化分配结果

    Figure  5.  Radar node selection and transmit resource optimization results of target 2 in scenario 1

    图  6  不同检测概率下的各目标BCRLB与跟踪精度阈值对比

    Figure  6.  The comparison between the BCRLB of targets and the specified tracking accuracy threshold

    图  7  仿真场景1不同检测概率下各目标ARMSE对比结果

    Figure  7.  Comparison of the ARMSE of targets with different values of probability of detection in scenario 1

    图  8  仿真场景1不同检测概率下总辐射功率消耗对比结果

    Figure  8.  Comparison of the total power consumption with different values of probability of detection in scenario 1

    图  9  仿真场景2中的目标RCS起伏模型

    Figure  9.  Target RCS undulation model in scenario 2

    图  10  仿真场景2目标1的雷达节点选择与辐射资源优化分配结果

    Figure  10.  Radar node selection and transmit resource optimization results of target 1 in scenario 2

    图  11  仿真场景2目标2的雷达节点选择与辐射资源优化分配结果

    Figure  11.  Radar node selection and transmit resource optimization results of target 2 in scenario 2

    图  12  仿真场景2不同检测概率下各目标ARMSE对比结果

    Figure  12.  Comparison of the ARMSE of targets with different values of probability of detection in scenario 2

    图  13  仿真场景2不同检测概率下总辐射功率消耗对比结果

    Figure  13.  Comparison of the total power consumption with different values of probability of detection in scenario 2

    图  14  仿真场景3多雷达网络分布与目标运动轨迹

    Figure  14.  Deployment of multiple radar networks and trajectories of moving targets in scenario 3

    图  15  仿真场景3目标1的雷达节点选择与辐射资源优化分配结果

    Figure  15.  Radar node selection and transmit resource optimization results of target 1 in scenario 3

    图  16  仿真场景3目标2的雷达节点选择与辐射资源优化分配结果

    Figure  16.  Radar node selection and transmit resource optimization results of target 2 in scenario 3

    图  17  仿真场景3不同检测概率下各目标ARMSE对比结果

    Figure  17.  Comparison of the ARMSE of targets with different values of probability of detection in scenario 3

    图  18  仿真场景3不同检测概率下总辐射功率消耗对比结果

    Figure  18.  Comparison of the total power consumption with different values of probability of detection in scenario 3

    图  19  仿真场景4目标1的雷达节点选择与辐射资源优化分配结果

    Figure  19.  Radar node selection and transmit resource optimization results of target 1 in scenario 4

    图  20  仿真场景4目标2的雷达节点选择与辐射资源优化分配结果

    Figure  20.  Radar node selection and transmit resource optimization results of target 2 in scenario 4

    图  21  仿真场景4不同检测概率下各目标ARMSE对比结果

    Figure  21.  Comparison of the ARMSE of targets with different values of probability of detection in scenario 4

    图  22  仿真场景4不同检测概率下总辐射功率消耗对比结果

    Figure  22.  Comparison of the total power consumption with different values of probability of detection in scenario 4

    图  23  不同场景下本文所提算法与穷举法的计算耗时对比

    Figure  23.  Comparison of computational time consumption between the proposed algorithm and the exhaustive method in different scenarios

    1  非理想检测下基于障碍函数法的雷达节点选择算法

    1.   Radar node selection algorithm based on barrier function method with imperfect detection

     输入:令$ {g_1} = {\kern 1pt} \mathbb{F}\left( {{\boldsymbol{\mu }}_k^q,{\boldsymbol{\hat P}}_{{\text{t}},k}^q,{\boldsymbol{\hat \beta }}_k^q} \right) - {\eta ^q} $, $ {g_2} = {\kern 1pt} - \mu _{1,1,k}^q $,
     $ {g_3} = {\kern 1pt} - \mu _{2,1,k}^q $, ···, $ {g_{{N_1} + 1}} = {\kern 1pt} - \mu _{{N_1},1,k}^q $, ···,
     $ {g_{\sum\limits_{m = 1}^M {{N_m}} + 1}} = {\kern 1pt} - \mu _{{N_M},M,k}^q $, $ {g_{\sum\limits_{m = 1}^M {{N_m}} + 2}} = {\kern 1pt} \mu _{1,1,k}^q - 1 $,
     $ {g_{\sum\limits_{m = 1}^M {{N_m}} + 3}} = {\kern 1pt} \mu _{2,1,k}^q - 1 $, ···, $ {g_{2\sum\limits_{m = 1}^M {{N_m}} + 1}} = {\kern 1pt} \mu _{{N_M},M,k}^q - 1 $,
     设置迭代索引$ \varphi = 1 $;设置可行域$ D = \left\{ {\mu _{n,m,k}^q\left| {{g_a}\left( {\mu _{n,m,k}^q} \right) \le 0,a = 1,2, \cdots ,2\displaystyle\sum\limits_{m = 1}^M {{N_m}} + 1} \right.} \right\} $,
     其中$ {g_a}\left( {\mu _{n,m,k}^q} \right) = {g_a} $;设置$ \varepsilon \gt 0 $为算法终止指标,$ {\xi ^{\left( \varphi \right)}} \gt 0 $,
     $ e \ge 2 $。
     步骤1:取$ {\left( {\mu _{n,m,k}^q} \right)^{\left( {\varphi - 1} \right)}} \in D $为初始点;
     步骤2:求解如下问题:
     $\begin{gathered} \min {\kern 1pt} {\mathbb{G}_1} - {\xi ^{\left( \varphi \right)} }\left[ {\frac{1}{ { {g_1} } } + \frac{1}{ { {g_2} } } + \cdots + \frac{1}{ { {g_{2 \sum\limits_{m = 1}^M { {N_m} } + 1} } } } } \right], \\ {\text{s} }{\text{.t} }{\text{.} }{\kern 1pt} {\kern 1pt} {\kern 1pt} \mu _{n,m,k}^q \in D. \\ \end{gathered}$
     式中,${\mathbb{G}_1}$表示优化模型(23)中的目标函数;
     步骤3:令上述问题的极小值点为$ {\left( {\mu _{n,m,k}^q} \right)^{\left( \varphi \right)}} $;
     步骤4:检验终止条件,若
     $ - {\xi ^{\left( \varphi \right)}}\left[ {\dfrac{1}{{{g_1}}} + \dfrac{1}{{{g_2}}} + , \cdots , + \dfrac{1}{{{g_{2\sum\limits_{m = 1}^M {{N_m}} + 1}}}}} \right] \lt \varepsilon $,算法终止;若
     未满足终止条件,令${\left( {\mu _{n,m,k}^q} \right)^{\left( {\varphi + 1} \right)} } \leftarrow \dfrac{ { { {\left( {\mu _{n,m,k}^q} \right)}^{\left( \varphi \right)} } } }{e}$,
     $ \varphi \leftarrow \varphi + 1 $。
     输出:雷达节点最优选择结果。
    下载: 导出CSV
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    [49] STOICA P and SELEN Y. Cyclic minimizers, majorization techniques, and the expectation-maximization algorithm: A refresher[J]. IEEE Signal Processing Magazine, 2004, 21(1): 112–114. doi: 10.1109/MSP.2004.1267055.
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出版历程
  • 收稿日期:  2023-05-09
  • 修回日期:  2023-07-14
  • 网络出版日期:  2023-08-01
  • 刊出日期:  2024-06-28

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