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

ZHAO Gang; SUN Nai-wei; SHEN Shen; YANG Yi-xin

doi:10.11993/j.issn.2096-3920.2022-0041

Volume 30 Issue 6

Dec 2022

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Article Navigation > Journal of Unmanned Undersea Systems > 2022 > 30(6): 774-786

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, 2022, 30(6): 774-786. 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, 2022, 30(6): 774-786. doi: 10.11993/j.issn.2096-3920.2022-0041

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

doi: 10.11993/j.issn.2096-3920.2022-0041

1.
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
2.
The 705 Research Institute, China State Shipbuilding Corporation Limited, Xi’an 710077, China

Received Date: 2022-08-08
Accepted Date: 2022-10-14
Rev Recd Date: 2022-09-20

Available Online: 2022-11-02

Abstract

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.
- underwater operation equipment,
- underwater acoustic go-back channel,
- Bayes-MCMC,
- echo-signal inversion

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References(17)

References

[1]	徐瑜, 倪小清, 夏红梅, 等. 蛙人探测声呐发展现状及关键技术[J]. 舰船电子工程, 2017, 37(3): 1-3,126. doi: 10.3969/j.issn.1672-9730.2017.03.001 Xu Yu, Ni Xiao-qing, Xia Hong-mei, et al. Current Status of Diver Detection Sonar and Key Technologies[J]. Ship Electronic Engineering, 2017, 37(3): 1-3,126. doi: 10.3969/j.issn.1672-9730.2017.03.001
[2]	邓新文, 叶浩亮. UUV声纳浅海声信道频率特性分析[J]. 鱼雷技术, 2013, 21(3): 184-188. Deng Xin-wen, Ye Hao-liang. Frequency Characteristics Analysis of Shallow Water Acoustic Channel for UUV Sonar[J]. Torpedo Technology, 2013, 21(3): 184-188.
[3]	管日升, 范新刚, 杨文瑞. 基于主动时反技术的水下目标探测仿真[J]. 舰船科学技术, 2020, 42(17): 135-139. doi: 10.3404/j.issn.1672-7649.2020.09.026 Guan Ri-sheng, Fan Xin-gang, Yang Wen-rui. Simulation of Underwater Target Detection Based on Active Time Reversal[J]. Ship Science and Technology, 2020, 42(17): 135-139. doi: 10.3404/j.issn.1672-7649.2020.09.026
[4]	李娜, 郝程鹏, 施博, 等. 水下修正空时自适应检测的性能分析[J]. 水下无人系统学报, 2018, 26(2): 133-139. Li Na, Hao Cheng-peng, Shi Bo, et al. Performance Analysis of Underwater Modified Space-Time Adaptive Detec- tion[J]. Journal of Unmanned Undersea Systems, 2018, 26(2): 133-139.
[5]	王奇, 王英民, 诸国磊. 基于特征提取的匹配场处理[J]. 火力与指挥控制, 2014, 39(5): 97-100,106. doi: 10.3969/j.issn.1002-0640.2014.05.024 Wang Qi, Wang Ying-min, Zhu Guo-lei. Matched Field Processing Based on Feature Extraction[J]. Fire Control & Command Control, 2014, 39(5): 97-100,106. doi: 10.3969/j.issn.1002-0640.2014.05.024
[6]	王海燕, 姜喆, 申晓红, 等. 两种水声信道模型中信噪比估计算法[J]. 西北工业大学学报, 2009, 27(3): 368-372. doi: 10.3969/j.issn.1000-2758.2009.03.016 Wang Hai-yan, Jiang Zhe, Shen Xiao-hong, et al. Modifying SNR-Independent Velocity Estimation Method to Make it Suitable for SNR Estimation in Shallow Water Acoustic Communication[J]. Journal of Northwestern Polytechnical University, 2009, 27(3): 368-372. doi: 10.3969/j.issn.1000-2758.2009.03.016
[7]	董阳泽, 许肖梅, 刘平香, 等. 浅海声信道建模及其应用研究[J]. 系统仿真学报, 2010, 22(1): 47-50, 55. doi: 10.16182/j.cnki.joss.2010.01.032 Dong Yang-ze, Xu Xiao-mei, Liu Ping-xiang, et al. Study on Modeling of Shallow Water Acoustic Channel and Its Applications[J]. Journal of System Simulation, 2010, 22(1): 47-50, 55. doi: 10.16182/j.cnki.joss.2010.01.032
[8]	Radosevic A, Proakis JG, Stojanovic M. Statistical Characterization and Capacity of Shallow Water Acoustic Channels [C]//OCEANS 2009. US: IEEE, 2009.
[9]	Yang T C. Properties of Underwater Acoustic Communication Channels in Shallow Water[J]. The Journal of the Acoustical Society of America, 2012, 131(1): 231-236.
[10]	许少华, 王皓, 王颖, 等. 一种改进的量子粒子群优化算法及其应用[J]. 计算机工程与应用, 2011, 47(20): 34-37. doi: 10.3778/j.issn.1002-8331.2011.20.010 Xu Shao-hua, Wang Hao, Wang Ying, et al. Improved Quantum Particle Swarm Optimization Algorithm and Its Application[J]. Computer Engineering and Applications, 2011, 47(20): 34-37. doi: 10.3778/j.issn.1002-8331.2011.20.010
[11]	Kim S M, Byun S H, Kim S G, et al. Experimental Analysis of Statistical Properties of Underwater Channel in a very Shallow Water Using Narrow and Broadband Signals[J]. OCEANS 2009. Biloxi, MS, United states, 2009
[12]	Yardim C. Estimation of Radio Refractivity from Radar Clutter Using Bayesian Monte Carlo Analysis[J]. IEEE Transactions on Antennas And Propagation, 2006, 54(4): 1318-1327. doi: 10.1109/TAP.2006.872673
[13]	Walsh B. Markov Chain Monte Carlo and Gibbs Sampling. Lexture Notes for EEB581[R]. [S.l.: s.n.], 2004.
[14]	马天政, 吕昊, 张义民. 一种基于Bayes方法的随机模型修正方法[J]. 工程设计学报, 2016, 23(3): 206-211. doi: 10.3785/j.issn.1006-754X.2016.03.003 Ma Tian-zheng, Lü Hao, Zhang Yi-min. Stochastic Model Updating Based on Bayesian Method[J]. Journal of Engineering Design, 2016, 23(3): 206-211. doi: 10.3785/j.issn.1006-754X.2016.03.003
[15]	高伟. 浅海混响统计反演海底声参数和界面统计参数研究[D]. 青岛: 中国海洋大学, 2008
[16]	盛峥, 黄思训, 曾国栋. 利用 Bayesian-MCMC 方法从雷达回波反演海洋波导[J]. 物理学报, 2009, 58(6): 4335-4341. doi: 10.3321/j.issn:1000-3290.2009.06.113 Sheng Zheng, Huang Si-Xun, Zeng Guo-Dong. Ocean Duct Inversion from Radar Clutter Using Bayesian-Markov chain Monte Carlo Method[J]. Acta Physica Sinica, 2009, 58(6): 4335-4341. doi: 10.3321/j.issn:1000-3290.2009.06.113
[17]	王升超, 韩立国, 巩向博, 等. 基于局部采样MCMC方法的时移探地雷达反演[J]. 地球物理学报, 2022, 65(3): 1135-1143. doi: 10.6038/cjg2022P0201 Wang Sheng-chao, Han Li-guo, Gong Xiang-bo, et al. Time-lapse Inversion of Ground Penetrating Radar (GPR) Based on Local Sampling MCMC Method[J]. Chinese Journal of Geophysics, 2022, 65(3): 1135-1143. doi: 10.6038/cjg2022P0201