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基于多分量LFM信号时频分析的水声多普勒和时延估计研究

宁更新 肖若君 谢靓

宁更新, 肖若君, 谢靓. 基于多分量LFM信号时频分析的水声多普勒和时延估计研究[J]. 电子与信息学报, 2024, 46(2): 688-696. doi: 10.11999/JEIT230068
引用本文: 宁更新, 肖若君, 谢靓. 基于多分量LFM信号时频分析的水声多普勒和时延估计研究[J]. 电子与信息学报, 2024, 46(2): 688-696. doi: 10.11999/JEIT230068
NING Gengxin, XIAO Ruojun, XIE Liang. Estimation of Underwater Acoustic Doppler Factor and time Delay based on time-frequency Analysis of multi-component LFM Signals[J]. Journal of Electronics & Information Technology, 2024, 46(2): 688-696. doi: 10.11999/JEIT230068
Citation: NING Gengxin, XIAO Ruojun, XIE Liang. Estimation of Underwater Acoustic Doppler Factor and time Delay based on time-frequency Analysis of multi-component LFM Signals[J]. Journal of Electronics & Information Technology, 2024, 46(2): 688-696. doi: 10.11999/JEIT230068

基于多分量LFM信号时频分析的水声多普勒和时延估计研究

doi: 10.11999/JEIT230068
基金项目: 国家自然科学基金 (61871191, 62192712, 62171187), 广东省基础与应用基础研究基金(2023A1515011139)
详细信息
    作者简介:

    宁更新:男,博士,副教授,研究方向为无线声通信、水声探测、语音信号处理(识别与增强)、阵列信号处理等

    肖若君:女,硕士生,研究方向为水声信号处理

    谢靓:女,硕士,研究方向为无线声通信

    通讯作者:

    宁更新 ninggx@scut.edu.cn

  • 中图分类号: TN929.3

Estimation of Underwater Acoustic Doppler Factor and time Delay based on time-frequency Analysis of multi-component LFM Signals

Funds: The National Natural Science Foundation of China (61871191, 62192712, 62171187), Guangdong Basic and Applied Basic Research Foundation (2023A1515011139)
  • 摘要: 在水声多普勒因子和时延估计研究实用化的进程中,利用多分量线性调频(LFM)信号实现估计的算法研究越来越普遍。针对多分量LFM信号时频域存有交叉项时各分量参数估计不准确的问题,提出基于非完全残差与脊线段匹配的自适应模态分解方法。该方法采用非完全残差函数保留了交叉点处的部分时频信息,利用脊线段匹配方法提供更精确的预设时频脊线,改进了各分量LFM信号调频斜率和起始频率的估计精度。联合两个估计量进一步给出了多普勒因子和时延估计的算法。仿真结果表示,较现有模态分解算法,所提改进方法有效解决了估计分量过程中交叉区间断裂带来的估计误差;水声多径的条件下,该方法的多普勒因子和时延估计精度优于对比的现有方法。
  • 图  1  ACMD算法结合完全残差处理有交叉的3分量LFM信号

    图  2  时频脊线片段匹配估计

    图  3  两种算法的自适应时频谱

    图  4  发送信号为单分量LFM信号的多普勒因子和时延估计均方误差(考虑多径)

    图  5  发送信号为时频域有交叉多分量LFM信号的多普勒因子和时延估计均方误差

    图  6  发送信号为时频域有交叉多分量LFM信号的多普勒因子和时延估计均值

    图  7  低频段时域有交叉多分量LFM信号的多普勒因子和时延估计均方误差

    图  8  低频段时频域有交叉多分量LFM信号的多普勒因子和时延估计均值

    表  1  IRSM-ACMD时频方法步骤

     步骤1:获取一个预分量。提取第1个分量时将X(t)赋值给非完全残差IR(t),提取第m个分量时依式(5)获取非完全残差IR(t),对IR(t)进行
     STFT得到时频分布TF(t,f),基于最大能量片段的寻找,利用式(12)—式(14)进行片段匹配后从TF(t,f)中获得其中1个分量的初始瞬时频率
     即预分量。
     步骤2:模态分解。根据预分量及非完全残差IR(t),利用式(3)—式(4)估计出一个分量$\tilde{x}_{{w} }(t)$和它的瞬时频率$ {\tilde{F}}_{m}\left(t\right) $、瞬时幅度$ {\tilde{A}}_{m}\left(t\right) $。
     并用最小二乘法拟合瞬时频率值,按式(8)更新IR(t)。
     步骤3:判断与循环。由式(6)判断所估计时频脊线是否重复,不重复的估计按式(7)更新到IRth(t)中,否则返回步骤1。由式(9)计算已提取
     分量信号的总能量,随后由式(10)判断估计是否结束,未结束返回步骤1继续估计,若结束则由式(11)获得分量估计的自适应时频谱。
    下载: 导出CSV

    表  2  发送信号为单分量LFM信号时多径信道对应的时延和多普勒因子仿真参数

    路径时延(s)多普勒因子
    路径1(L1)0.4562–0.0898
    路径2(L2)0.1230.028
    路径3(L3)3.66560.0898
    下载: 导出CSV

    表  3  发送信号为时频域有交叉多分量LFM信号时的仿真参数

    仿真参数分量1分量2分量3
    调频斜率(kHz/s)1.173.71.7
    起始频率(kHz)403545
    截止频率(kHz)475755
    多普勒因子0.0250.0350.045
    时延(s)1.216 20.902 61.465 6
    下载: 导出CSV

    表  4  低频段时频域有交叉多分量LFM信号的仿真参数

    仿真参数分量1分量2分量3
    调频斜率(Hz/s)800–7001200
    起始频率(Hz)17001400800
    截止频率(Hz)25007002000
    多普勒因子0.00250.00350.0045
    时延(s)0.514 30.543 20.516 6
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-02-20
  • 修回日期:  2023-09-27
  • 网络出版日期:  2023-10-10
  • 刊出日期:  2024-02-29

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