A Direction of Arrival Estimation Algorithm for Underwater Distributed Source based on Deep Neural Network

LIANG Yinian; LI Jie; CHEN Fangjiong; JI Fei; YU Hua

doi:10.11993/j.issn.2096-3920.2024-0178

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LIANG Yinian, LI Jie, CHEN Fangjiong, JI Fei, YU Hua. A Direction of Arrival Estimation Algorithm for Underwater Distributed Source based on Deep Neural Network[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2024-0178

Citation:

LIANG Yinian, LI Jie, CHEN Fangjiong, JI Fei, YU Hua. A Direction of Arrival Estimation Algorithm for Underwater Distributed Source based on Deep Neural Network[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2024-0178

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A Direction of Arrival Estimation Algorithm for Underwater Distributed Source based on Deep Neural Network

doi: 10.11993/j.issn.2096-3920.2024-0178

School of Electronics and Information Engineering, South China University of Technology Guangzhou, 510641, China

Received Date: 2024-12-24
Accepted Date: 2025-02-08
Rev Recd Date: 2025-01-28

Available Online: 2025-02-25

Abstract

Abstract

Regarding the issue that traditional subspace-based direction-of-arrival (DOA) estimation algorithms for distributed sources with varying coherences require prior knowledge of the distributed sources’ the coherent coefficient, a DOA estimation algorithm for underwater distributed sources based on deep learning is proposed. This method utilizes the separable relationship between temporal and angular components in the partially coherent distributed (PCD) source signal model to simplify the model by piecewise mean normalization method . Then, a designed deep neural network is trained for different coherent coefficient data, which enhances the DOA estimation ability for different coherence distributed sources. Simulation results indicate that this method can effectively estimate the distributed sources with different coherent coefficients without prior knowledge. The root mean square error (RMSE) results of the proposed method are compared with four traditional subspace-based methods and one deep convolutional neural network distributed source DOA estimation method versus different SNRs, snapshots, and coherent coefficients. The results show that the RMSE of the proposed method under the coherently distributed source case is an average of 0.42° superior to the other methods versus different SNRs and snapshots. Under the incoherently distributed source case, the RMSE results of the proposed method are better than the other methods by 0.04° on average, with SNR greater than zero and snapshots greater than 600. Generally, the proposed method is superior to the other methods under different coherence coefficients.
- direction of arrival estimation,
- distributed source,
- target localization,
- deep learning

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References(20)

References

[1]	周烨, 温玮, 韩建辉, 等. 基于声固耦合的水下复杂目标声散射研究[J]. 水下无人系统学报, 2020, 28(1): 51-56. ZHOU Y, WEN W, HAN J H, et al. Research on Acoustic Scattering of Underwater Complicated Target Based on Sound-Solid Coupling[J]. Journal of Unmanned Undersea Systems, 2020, 28(1): 51-56.
[2]	曹浩, 樊书宏, 周晶. 基于板块元法的鱼雷声散射特性分析[J]. 水下无人系统学报, 2022, 30(2): 184-189. doi: 10.11993/j.issn.2096-3920.2022.02.007 CAO H, FAN S H, ZHOU J, et al. Analysis of Acoustic Scattering Characteristics of a Torpedo Based on Planar Elements Method[J]. Journal of Unmanned Undersea Sys- tems, 2022, 30(2): 184-189. doi: 10.11993/j.issn.2096-3920.2022.02.007
[3]	杜金香, 许恒博, 祝鹏. 一种角度加权的最小二乘目标定位算法[J]. 水下无人系统学报, 2019, 27(5): 570-573. DU J X, XU H B, ZHU P. An Angle Weighted Least Squares Algorithm for Target Localization[J]. Journal of Unmanned Undersea Systems, 2019, 27(5): 570-573.
[4]	周敏. 相干分布式目标的方位估计[J]. 水下无人系统学报, 2008, 16(1): 036-39. ZHOU M. Direction of Arrival Estimation for Coherent Distributed Target Sources[J]. Journal of Unmanned Undersea Systems, 2008, 16(1): 036-39.
[5]	S. Valaee, B. Champagne, P. Kabal. Parametric localization of distributed sources[J]. IEEE Transactions on Signal Processing, 1995, 43(9), 2144-2153.
[6]	梁奕念, 李杰, 龙力榕, 陈芳炯. 利用卷积神经网络的非相干分布式源定位方法[J]. 声学学报, 2024, 49(1): 38-48. doi: 10.12395/0371-0025.2022138 LIANG Y N, LI J, LONG L R, et al. Incoherently distributed sources localization using convolutional neural network[J]. ACTA Acustica, 2024, 49(1): 38-48. doi: 10.12395/0371-0025.2022138
[7]	RAICH R, GOLDBERG J, MESSER H. Bearing estimation for a distributed source: Modeling, inherent accuracy limitations and algorithms[J]. IEEE Transactions on Signal Processing, 2000, 48(2): 429-441.
[8]	MENG Y, STOICA P, WONG K M. Estimation of the directions of arrival of spatially dispersed signals in array processing[J]. IEEE Proceedings Radar, Sonar and Navigation, 1996, 143(1): 1-9.
[9]	梁浩, 崔琛, 代林, 等. 基于ESPRIT算法的L型阵列MIMO雷达降维DOA估计[J]. 电子与信息学报, 2015, 37(8): 1828-1835. LIANG H, CUI C, DAI L, et al. Reduced-dimensional DOA estimation based on ESPRIT algorithm in MIMO radar with L-shaped array[J]. Journal of Electronics & Information Technology, 2015, 37(8): 1828-1835.
[10]	王绪虎, 陈建峰, 叶永, 等. 基于ESPRIT算法的矢量水听器阵方位估计性能分析[C]//中国造船工程学会电子技术学术委员会——2012年水下复杂战场环境目标识别与对抗及仿真技术学术交流会.
[11]	SHAHBAZPANAHI S, VALAEE S, BASTANI M H. Distributed source localization using ESPRIT algorithm[J]. IEEE Transactions on Signal Processing, 2001, 49(10): 2169-2178.
[12]	CAO R, GAO F, ZHANG X. An angular parameter estimation method for incoherently distributed sources via generalized shift invariance[J]. IEEE Transaction Signal Processing, 2016, 64(17): 4493-4503. doi: 10.1109/TSP.2016.2557312
[13]	WANG Y, A. Zoubir, Some new techniques of localization of spatially distributed sources[C]// 2007 Conference Record of the Forty First Asilomar Conference on Signals, Systems and Computers. IEEE, 2007, pp. 1807-1811.
[14]	ZOUBIR A, WANG Y, CHARGÉ P. Efficient subspace-based estimator for localization of multiple incoherently distributed sources. IEEE Transaction Signal Processing, 2008, 56(2): 532-542.
[15]	PAPAGEORGIOU G K, SELLATHURAI M, ELDAR Y C. Deep networks for direction-of-arrival estimation in low SNR[J]. IEEE Transactions on Signal Processing, 2021, 69: 3714-3729.
[16]	FEINTUCH S, TABRIKIAN J, BILIK I, et al. Neural-Network-Based DOA estimation in the presence of Non-Gaussian interference[J]. IEEE Transactions on Aerospace and Electronic Systems, 2023, 60(1): 119-132.
[17]	TIAN Y, LIU S, LIU W, et al. Vehicle positioning with deep-learning-based direction-of-arrival estimation of incoherently distributed sources[J]. IEEE Internet of Things Journal, 2022, 9(20): 20083-20095. doi: 10.1109/JIOT.2022.3171820
[18]	WANG J, QUAN T, JIAO L, et al. Doa estimation of underwater acoustic array signal based on wavelet transform with double branch convolutional neural network[J]. IEEE Transactions on Vehicular Technology, 2023, 72(5): 5962-5972. doi: 10.1109/TVT.2022.3203034
[19]	GE S, LI K, Rum, S. N. B. M. Deep learning approach in DOA estimation[J]. A systematic literature review. Mobile Information Systems, 2021(1): 6392875.
[20]	ZHANG C L, LUO J H, WEI X S, et al. In defense of fully connected layers in visual representation transfer[C]//18th Pacific-Rim Conference on Multimedia (PCM), Harbin, China:PCM, 2018: 807-817.