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水声双程信道建模及自适应采样反演

赵罡 孙乃葳 申珅 杨益新

赵罡, 孙乃葳, 申珅, 等. 水声双程信道建模及自适应采样反演[J]. 水下无人系统学报, 2022, 30(6): 1-14 doi: 10.11993/j.issn.2096-3920.2022-0041
引用本文: 赵罡, 孙乃葳, 申珅, 等. 水声双程信道建模及自适应采样反演[J]. 水下无人系统学报, 2022, 30(6): 1-14 doi: 10.11993/j.issn.2096-3920.2022-0041
ZHAO Gang, SUN Nai-wei, SHEN Shen, YANG Yi-xin. Parameter Adaptive Sampling Inversion of Underwater Acoustic Go-back Channel Model Based on Bayes-MCMC[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2022-0041
Citation: ZHAO Gang, SUN Nai-wei, SHEN Shen, YANG Yi-xin. Parameter Adaptive Sampling Inversion of Underwater Acoustic Go-back Channel Model Based on Bayes-MCMC[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2022-0041

水声双程信道建模及自适应采样反演

doi: 10.11993/j.issn.2096-3920.2022-0041
详细信息
    作者简介:

    赵罡:赵 罡(1976-), 男, 博士研究生, 主要从事水中兵器自导仿真研究, E-mail: ggz216@sina.com

    通讯作者:

    杨益新(1975-), 男, 教授, 主要从事水声信号处理研究

  • 中图分类号: TJ630

Parameter Adaptive Sampling Inversion of Underwater Acoustic Go-back Channel Model Based on Bayes-MCMC

  • 摘要: 高置信度水声双程信道仿真建模是水下目标回波仿真研究的基础内容, 对水下作战装备的研发具有重要作用。文中基于经典信道模型通过合理的假设建立了水声双程信道解析模型。在此基础上, 以Bayes-MCMC反演算法为核心, 分析水声双程信道参数反演问题特点, 设计了M-H自适应单维度串行采样算法, 实现了基于回波信号的信道模型参数高效反演。经仿真及实测数据验证, 所设计的自适应采样反演方法其结果具有较好的一致性和收敛性, 在水下作战装备仿真测试方面具有良好的工程应用前景。

     

  • 图  1  水声双程信道反演算法基本流程

    Figure  1.  Basic process of underwater acoustic go-back channel inversion algorithm

    图  2  MCMC采样结果及其与真值分布对比

    Figure  2.  MCMC Sampling result and comparison with true distribution

    图  3  模型参数采样过程及反演结果与真值分布对比

    Figure  3.  Sampling Result of Model Parameter and Comparison of Inversion Result with true Distribution

    图  4  不同SNR下脉冲回波时域信号

    Figure  4.  Pulse echo time domain signals under different signal-to-noise ratios

    图  5  不同SNR信号反演结果与真值分布对比

    Figure  5.  Comparison of inversion result with true distribution under different SNR signals

    图  6  多样本脉冲回波时域信号

    Figure  6.  Multiple groups of pulse time domain echo

    图  7  1~8组信号反演结果与真值分布对比

    Figure  7.  Comparison of inversion result with true distribution at 1th-to-8th groups

    图  8  1~8组反演参数变化趋势

    Figure  8.  Change trend of inversion parameters at 1th-to-8th groups

    图  9  第5、9组双程信道模型参数反演过程结果对比

    Figure  9.  Comparison of Parameter Inversion Result of Go-back Channel Model at 5th&9th Groups

    图  10  第5、9组信号反演结果与真值分布对比

    Figure  10.  Comparison of inversion result with true distribution at the 5th&9th groups

    表  1  不同SNR信号参数反演结果对比

    Table  1.   Comparison of parameter inversion result under different SNR signals

    SNR/dB$ \overline {{\sigma ^2}} $$D({\sigma ^2})$$ \overline K $$D(K)$
    60.5950.0031.0250.01
    10.4879.47×10−40.8520.008
    SNR/dB$ \overline {{D_K}{L_{{Z_{\text{2}}}}}} $$ D(\overline {{D_K}{L_{{Z_{\text{2}}}}}} ) $$ \overline {{{\tilde D}_{{Z_{\text{2}}}}}} $$ D({\tilde D_{{Z_{\text{2}}}}}) $
    60.2160.0091.73%1.07×10−4
    10.3220.00028.05%5.5×10−5
    下载: 导出CSV

    表  2  1~8组双程信道模型参数反演结果

    Table  2.   Parameter inversion result of go-back channel model at 1th-to-8th groups

    样本组数$ \overline {{\sigma ^2}} $$ \overline K $$ \overline {{D_K}{L_{{Z_{\text{2}}}}}} $$ \overline {{{\tilde D}_{{Z_{\text{2}}}}}} $
    10.3131.8330.8783.2%
    20.3031.8301.0554.1%
    30.3021.8530.8323.4%
    40.2981.8630.8133.3%
    50.3381.8140.9013.8%
    60.2951.8561.2604.3%
    70.3041.8250.9333.5%
    80.3141.8290.8503.4%
    下载: 导出CSV

    表  3  第5、9组信号参数反演结果对比

    Table  3.   Comparison of inversion result at 5th&9th groups

    样本组数$ \overline {{\sigma ^2}} $$D({\sigma ^2})$$ \overline K $$D(K)$
    50.3536.72×10−41.8160.001
    90.2872.15×10−41.8540.0004
    样本组数$ \overline {{D_K}{L_{{Z_{\text{2}}}}}} $$ D(\overline {{D_K}{L_{{Z_{\text{2}}}}}} ) $$ \overline {{{\tilde D}_{{Z_{\text{2}}}}}} $$ D({\tilde D_{{Z_{\text{2}}}}}) $
    50.9010.1173.83%9.19×10−5
    90.5470.0692.65%7.67×10−5
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-08-08
  • 录用日期:  2022-10-14
  • 修回日期:  2022-09-20
  • 网络出版日期:  2022-11-02

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