杂波先验数据缺失条件下基于级联优化处理的雷达波形设计方法

张应奎 孙国皓 钟苏川 余显祥

张应奎, 孙国皓, 钟苏川, 等. 杂波先验数据缺失条件下基于级联优化处理的雷达波形设计方法[J]. 雷达学报, 待出版. doi: 10.12000/JR22166
引用本文: 张应奎, 孙国皓, 钟苏川, 等. 杂波先验数据缺失条件下基于级联优化处理的雷达波形设计方法[J]. 雷达学报, 待出版. doi: 10.12000/JR22166
ZHANG Yingkui, SUN Guohao, ZHONG Suchuan, et al. Radar waveform design method based on cascade optimization processing under missing clutter prior data[J]. Journal of Radars, in press. doi: 10.12000/JR22166
Citation: ZHANG Yingkui, SUN Guohao, ZHONG Suchuan, et al. Radar waveform design method based on cascade optimization processing under missing clutter prior data[J]. Journal of Radars, in press. doi: 10.12000/JR22166

杂波先验数据缺失条件下基于级联优化处理的雷达波形设计方法

doi: 10.12000/JR22166
基金项目: 国家自然科学基金(62201371),四川省自然科学基金(2022NSFSC1952)
详细信息
    作者简介:

    张应奎,硕士生,主要研究方向为基于机器学习的雷达信号处理方法

    孙国皓,博士,副研究员,主要研究方向为认知雷达信号处理、分布式雷达信号处理、机载/星载雷达信号处理、天域态势感知

    钟苏川,副研究员,主要研究方向为随机动力系统、随机信号处理等

    余显祥,博士后,主要研究方向为雷达波形设计与处理、最优化理论算法以及阵列信号处理等

    通讯作者:

    孙国皓 sghsjw2005@126.com

  • 责任主编:唐波 Corresponding Editor: TANG Bo
  • 中图分类号: TN958

Radar Waveform Design Method Based on Cascade Optimization Processing under Missing Clutter Prior Data

Funds: The National Natural Science Foundation of China (62201371), Sichuan Provincial Natural Science Foundation (2022NSFSC1952)
More Information
  • 摘要: 认知雷达波形设计往往依赖于精准的杂波先验信息,当先验信息数据存在缺失时,所构建的杂波模型会严重失配,进而影响雷达对杂波的抑制能力。该文针对杂波先验数据缺失条件下的雷达波形优化问题,建立完全随机缺失机制下的点状与块状缺失场景,设计恒模与相似性约束的波形优化模型,提出基于优先级填充-强化学习级联优化的雷达波形训练算法:即采用强化学习智能体与填充算法修复后的杂波环境相交互的级联方法,以最大化信杂噪比为优化目标,通过迭代训练得到雷达最佳波形参数配置策略。最后,仿真验证不同缺失概率条件下所提算法的优越性。结果表明:相比于传统非级联优化算法,该文所提算法均可获得更优的杂波抑制性能,有效提升雷达的探测能力。

     

  • 图  1  缺失场景示意图

    Figure  1.  Schematic diagram of the missing scene

    图  2  级联优化算法整体框架图

    Figure  2.  Overall framework diagarm of the cascade optimization algorithm

    图  3  数据修复结构原理图

    Figure  3.  Schematic diagram of data repair structure

    图  4  雷达波形设计的DDPG算法结构图

    Figure  4.  Structure diagram of DDPG algorithm for radar waveform design

    图  5  杂波参考矩阵可视化图

    Figure  5.  Visualization of the clutter reference matrix

    图  6  杂波数据信息缺失图

    Figure  6.  Missing information of clutter data

    图  7  点状缺失场景下缺失数据修复图

    Figure  7.  Missing data repair diagram in the point-like missing scene

    图  8  块状缺失场景下缺失数据修复图

    Figure  8.  Missing data repair diagram in the block-like missing scenario

    图  9  不同缺失概率下数据修复性能分析

    Figure  9.  Data repair performance analysis under different missing probability

    图  10  仅恒模约束下强化学习奖励曲线图

    Figure  10.  Reinforcement learning reward curves under constant modulus constraint

    图  11  仅恒模约束下不同场景的输出SCNR性能曲线图

    Figure  11.  Output SCNR performance curves of different scenarios under constant modulus constraint

    图  12  相似性约束下强化学习奖励曲线图

    Figure  12.  Reinforcement learning reward curves under similarity constraints

    图  13  相似性约束下不同场景的输出SCNR性能曲线图

    Figure  13.  Output SCNR performance curves of different scenarios under similarity constraints

    表  1  优先级填充算法

    Table  1.   Priority filling algorithm

     输入:杂波缺失矩阵$ \tilde {\boldsymbol C} $,滑窗维度M
     输出:杂波修复矩阵$\hat {\boldsymbol{C}}$
     1:将杂波缺失矩阵$ \tilde {\boldsymbol C} $分为目标区域${\boldsymbol{\varOmega}}$和源区域${\boldsymbol{\varPhi}}$
     2:根据式(13)初始化置信度$C(p)$
     3:识别目标区域轮廓${\boldsymbol{\delta \varOmega}}$
     4:根据式(10)计算优先级$P(p)$, $\forall p \in {\boldsymbol{\delta \varOmega}}$
     5:找到优先级最高的待填充样本$ {{\boldsymbol{\varPsi}} _{\hat p}} \in {\mathbb{C}^{M \times M}} $,即
       $\hat p = \arg \mathop {\max }\limits_{p \in {\boldsymbol{\delta \varOmega } } }P(p)$
     6:根据式(14)得到最相似样本$ {{\boldsymbol{\varPsi}} _{\hat q}} $
     7:将最相似样本$ {{\boldsymbol{\varPsi}} _{\hat q}} $内的数据信息复制到$ {{\boldsymbol{\varPsi}} _{\hat p}} $内
     8:根据式(15)更新置信度$C(p)$
     9:判断${\boldsymbol{\varOmega}}$是否为空集,如果是,算法结束;否则跳转3
    下载: 导出CSV

    表  2  基于DDPG的波形优化算法

    Table  2.   Algorithm for waveform optimization based on DDPG

     输入:Actor策略网络及其目标网络,Critic评估网络及其目标网络,网络参数分别为$\theta ,{\theta '},\omega ,{\omega'}$,奖励衰减因子$\gamma $,软更新系数$\tau $,最大迭
        代次数T,经验回放池${ \rm{R} }$,采样样本数K
     输出:最佳Actor策略网络${\pi _*}(a|s)$
     1:随机初始化Actor策略网络参数$\theta $和Critic评估网络参数$\omega $
     2:初始化目标网络参数${\theta'} = \theta$, ${\omega'} = \omega$
     3:初始化经验回放池${{R} }$
     4:for 回合$e \in \{ 1,2, \cdots ,T\} $ do
     5: 初始化随机噪声$ \mathcal{N} $,初始化状态s
     6: 根据式(16)得到Actor网络的输出动作${a_t}$
     7: 执行动作${a_t}$,获得下一时刻状态${s_{t + 1}}$,反馈奖励${r_t}$
     8: 将$\left\{ {{s_t},{a_t},{r_t},{s_{t + 1}}} \right\}$存入经验回放池${ \rm{R} }$
     9: 从经验回放池中随机采样K个经验样本$\left\{ {{s_i},{a_i},{r_i},{s_{i + 1}}} \right\}$, $i = 1,2, \cdots ,K$
     10: 根据式(17)和式(18)更新Actor策略网络和Critic评估网络
     11: 根据式(19)更新目标网络参数${\theta '}$和${\omega '}$
     12: 判断${s_{t + 1}}$是否为终止状态,如果是,迭代完毕,否则跳转步骤5
    下载: 导出CSV

    表  3  强化学习参数表

    Table  3.   Reinforcement learning parameters table

    参数数值
    经验池大小200000
    训练批次大小64
    训练总次数100000
    回合训练次数1000
    学习率0.001
    惩罚因子0.98
    Actor, Critic网络层节点数[400, 300]
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-08-09
  • 修回日期:  2022-10-24
  • 网络出版日期:  2022-11-03

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