Parameter Adaptive Sampling Inversion of Underwater Acoustic Go-back Channel Model Based on Bayes-MCMC
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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Metropolis-Hastings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。
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关键词:
- 水下作战装备 /
- 水声双程信道 /
- Bayes-MCMC /
- 回波信号反演
Abstract: High-confidence underwater acoustic go-back channel modeling is an essential part of the study of target echo simulation and plays an important role in the development of underwater operation equipment. Based on the classical channel model and reasonable assumptions, an analytical model of an underwater acoustic go-back channel is established. Using the Bayes-MCMC inversion algorithm as the core, the characteristics of the inversion problem of underwater acoustic channel parameters were analyzed, and the Metropolis-Hastings adaptive single-dimension serial sampling algorithm was designed to realize efficient channel model parameter inversion based on echo signals. The results of the simulation and measured data show that the proposed adaptive sampling inversion method has good consistency and convergence and has good engineering application prospects in underwater operation equipment simulation tests. -
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 chan- nel inversion algorithm
图 2 MCMC采样结果及其与真值分布对比
Figure 2. MCMC Sampling results and comparison with their true distributions
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 process of model parameter and comparison of inversion results with true distributions
图 5 不同SNR信号反演结果与真值分布对比
Figure 5. Comparison of inversion results with true distributions under different SNR signals
7 1~8组信号反演结果与真值分布对比
7. Comparison of inversion results with true distributions at 1th to 8th groups signals
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 signals
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 results of go-back channel model at 5th & 9th groups signals
图 10 第5、9组信号反演结果与真值分布对比
Figure 10. Comparison of inversion results with true distributions at the 5th & 9th groups signals
表 1 不同SNR信号参数反演结果对比
Table 1. Comparison of parameter inversion results under different SNR signals
SNR/dB $ \overline {{\sigma ^2}} $ $D({\sigma ^2})$ $ \overline K $ $D(K)$ 6 0.595 0.003 1.025 0.01 1 0.487 9.47×10−4 0.852 0.008 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}}}}}) $ 6 0.216 0.009 1.73% 1.07×10−4 1 0.322 0.0002 8.05% 5.5×10−5 
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表 2 1~8组双程信道模型参数反演结果
Table 2. Parameter inversion results of go-back channel model at 1th to 8th groups signals
样本组数 $ \overline {{\sigma ^2}} $ $ \overline K $ $ \overline {{D_K}{L_{{Z_{\text{2}}}}}} $ $ \overline {{{\tilde D}_{{Z_{\text{2}}}}}} $ 1 0.313 1.833 0.878 3.2% 2 0.303 1.830 1.055 4.1% 3 0.302 1.853 0.832 3.4% 4 0.298 1.863 0.813 3.3% 5 0.338 1.814 0.901 3.8% 6 0.295 1.856 1.260 4.3% 7 0.304 1.825 0.933 3.5% 8 0.314 1.829 0.850 3.4%
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表 3 第5、9组信号参数反演结果对比
Table 3. Comparison of inversion results at 5th & 9th groups signals
样本组数 $ \overline {{\sigma ^2}} $ $D({\sigma ^2})$ $ \overline K $ $D(K)$ 5 0.353 6.72×10−4 1.816 0.001 9 0.287 2.15×10−4 1.854 0.0004 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}}}}}) $ 5 0.901 0.117 3.83% 9.19×10−5 9 0.547 0.069 2.65% 7.67×10−5
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