基于时频稀疏先验的天波超视距雷达瞬态干扰抑制算法

陈子睿 计一飞 刘喜旺 张永胜 董臻 陈阿磊 刘维建

陈子睿, 计一飞, 刘喜旺, 等. 基于时频稀疏先验的天波超视距雷达瞬态干扰抑制算法[J]. 雷达学报(中英文), 待出版. doi: 10.12000/JR24188
引用本文: 陈子睿, 计一飞, 刘喜旺, 等. 基于时频稀疏先验的天波超视距雷达瞬态干扰抑制算法[J]. 雷达学报(中英文), 待出版. doi: 10.12000/JR24188
CHEN Zirui, JI Yifei, LIU Xiwang, et al. Transient interference suppression algorithm based on time frequency sparse prior for skywave OTHR[J]. Journal of Radars, in press. doi: 10.12000/JR24188
Citation: CHEN Zirui, JI Yifei, LIU Xiwang, et al. Transient interference suppression algorithm based on time frequency sparse prior for skywave OTHR[J]. Journal of Radars, in press. doi: 10.12000/JR24188

基于时频稀疏先验的天波超视距雷达瞬态干扰抑制算法

DOI: 10.12000/JR24188
基金项目: 国家自然科学基金(62101568, 62371460, 62471474),湖南省自然科学基金(2024JJ4046),博士后创新人才支持计划(BX20230473),湖南省科技创新计划资助-湖湘青年英才(2024RC3122)
详细信息
    作者简介:

    陈子睿,博士生,主要研究方向为天波超视距雷达、压缩感知和电离层传播效应

    计一飞,副教授,主要研究方向为SAR信号处理、电离层传播效应

    刘喜旺,工程师,主要研究方向为雷达信号处理、目标检测及跟踪

    张永胜,正高级工程师,博士生导师,主要研究方向为SAR系统设计与信号处理

    董 臻,研究员,博士生导师,主要研究方向为SAR系统设计和处理、地面动目标监测和数字波束形成等

    陈阿磊,副教授,主要研究方向为雷达系统、目标检测和天波超视距雷达

    刘维建,副教授,主要研究方向为多通道信号检测、统计和阵列信号处理

    通讯作者:

    计一飞 jyfnudt@163.com

  • 责任主编:王增福 Corresponding Editor: WANG Zengfu
  • 中图分类号: TN958.93

Transient Interference Suppression Algorithm Based on Time Frequency Sparse Prior for Skywave OTHR

Funds: The National Natural Science Foundation of China (62101568, 62371460, 62471474), National Natural Science Foundation of Hunan Province, China (2024JJ4046), Postdoctoral Innovative Talents Support Program (BX20230473), Science and Technology Innovation Program of Hunan Province (2024RC3122)
More Information
  • 摘要: 针对瞬态干扰严重影响天波超视距雷达(OTHR)目标检测性能的问题,提出了一种基于时频稀疏先验(TFSP)的瞬态干扰抑制算法。TFSP同时利用了瞬态干扰在慢时域的稀疏先验以及海杂波和目标在多普勒频域的稀疏先验构造目标函数,通过交替方向乘子法(ADMM)进行最优化以实现瞬态干扰抑制。不同于现有算法“干扰定位—剔除—数据恢复”的处理步骤,TFSP能够直接分离瞬态干扰分量并恢复无干扰频谱。最后,通过OTHR实测数据实验验证了TFSP在对海和对空模式下均能得到良好的瞬态干扰抑制结果,相比于多数现有方法,TFSP具有更高的输出信噪比(SNR)以及更高的运算效率,其输出SNR增加了3~5 dB,运算复杂度仅为线性对数阶。

     

  • 图  1  对空模式下OTHR实测数据

    Figure  1.  OTHR measured data in air mode

    图  2  CS瞬态干扰抑制方法示意图

    Figure  2.  Schematic diagram of CS transient interference suppression method

    图  3  TFSP瞬态干扰抑制方法示意图

    Figure  3.  Schematic diagram of TFSP transient interference suppression method

    图  4  两种探测模式下改变$ {\gamma _1} $与$ \gamma $的NMSE

    Figure  4.  NMSE when changing $ {\gamma _1} $ and $ \gamma $ in two detection modes

    图  5  对海模式下慢时域TFSP瞬态干扰抑制结果

    Figure  5.  Sea mode TFSP interference suppression result in slow time domain

    图  6  对海模式下各算法RD域瞬态干扰抑制结果

    Figure  6.  Sea mode transient interference suppression results of each algorithm in RD domain

    图  7  对海模式下各算法多普勒域瞬态干扰抑制结果

    Figure  7.  Sea mode transient interference suppression results of each algorithm in Doppler domain

    图  8  对空模式下慢时域TFSP瞬态干扰抑制结果

    Figure  8.  Air mode TFSP interference suppression result in slow time domain

    图  9  对空模式下各算法RD域瞬态干扰抑制结果

    Figure  9.  Air mode transient interference suppression results of each algorithm in RD domain

    图  10  对空模式下各算法多普勒域瞬态干扰抑制结果

    Figure  10.  Air mode transient interference suppression results of each algorithm in Doppler domain

    图  11  乘法次数随向量维度变化的曲线

    Figure  11.  The curve of multiplication times changing with vector dimension

    1  TFSP算法

    1.   TFSP algorithm

     输入:受瞬态干扰的慢时域信号y、参数$\gamma $, ${\gamma _1}$, $\rho = 0.1$,最大
     迭代次数$K = 100$;
     输出:慢时域瞬态干扰分量o、纯净频谱x
     初始化:初始向量$ {\boldsymbol{o}} = {\bf{0}} $, $ {\boldsymbol{x}} = {\bf{0}} $, $ {\boldsymbol{n}} = {\bf{0}} $, $ {\boldsymbol{b}} = {\bf{0}} $,中间变量
     $ {\boldsymbol{u}} = {\bf{0}} $,初始化迭代次数$k = 1$;
     执行迭代$k = 1,2, \cdots ,K$
     计算梯度$ \nabla {g^k}\left( {\boldsymbol{o}} \right) = {{\boldsymbol{o}}^k} - {\boldsymbol{y}} + \sqrt M {\mathrm{IFFT}}\left( {{{\boldsymbol{x}}^k} + {{\boldsymbol{n}}^k} - {{\boldsymbol{b}}^k}/\rho } \right) $;
     通过式(18)更新$ {{\boldsymbol{o}}^{k + 1}} $;
     更新中间变量$ {{\boldsymbol{u}}^{k + 1}} = {\mathrm{FFT}}\left( {{\boldsymbol{y}} - {{\boldsymbol{o}}^{k + 1}}} \right)/\sqrt M $
     更新$ {{\boldsymbol{x}}^{k + 1}} = {\mathcal{P}_{\left( {{\gamma _1}/\rho } \right){\ell _1}}}\left( {{{\boldsymbol{u}}^{k + 1}} - {{\boldsymbol{n}}^k} + {{\boldsymbol{b}}^k}/\rho } \right) $;
     更新$ {{\boldsymbol{n}}^{k + 1}} = \left( {{{\boldsymbol{b}}^k} + \rho \left( {{{\boldsymbol{u}}^{k + 1}} - {{\boldsymbol{x}}^{k + 1}}} \right)} \right)/\left( {\rho + 1} \right) $;
     更新$ {{\boldsymbol{b}}^{k + 1}} = {{\boldsymbol{b}}^k} + \tau \rho \left( {{{\boldsymbol{u}}^{k + 1}} - {{\boldsymbol{x}}^{k + 1}} - {\boldsymbol{{n}}^{k + 1}}} \right) $;
     更新迭代次数$k = k + 1$;
     当满足$k > K$或$\left\| {{{\boldsymbol{x}}^{k + 1}} - {{\boldsymbol{x}}^k}} \right\|_2^2/\left\| {{{\boldsymbol{x}}^k}} \right\|_2^2 < {10^{ - 6}}$时;
     停止迭代
    下载: 导出CSV

    表  1  各算法的运行时间(s)

    Table  1.   Running time of each algorithm (s)

    算法对海模式的运行时间对空模式的运行时间
    FFT(对比)0.0210.007
    AR模型0.130.05
    鲁棒平滑572.513.27
    PRCA-SVT3252.7842.03
    IALM15521.4373.24
    FACAMP17.510.87
    TFSP3.420.72
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-09-14
  • 修回日期:  2024-11-02
  • 网络出版日期:  2024-11-18

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