一种基于深度神经网络的水下分布源波达方向估计算法

梁奕念; 李杰; 陈芳炯; 季飞; 余华

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

一种基于深度神经网络的水下分布源波达方向估计算法

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

华南理工大学电子与信息学院, 广东广州, 510641

基金项目: 国家自然科学基金(62271208; 62192712; 62341129); 广州市重点研发项目(2023B03J1308).

详细信息

作者简介:
梁奕念(1993-), 女, 博士研究生, 主要研究方向为深度学习在水声阵列信号处理中的研究

中图分类号: TN911.7, TN929.3
计量
- 文章访问数: 12
- HTML全文浏览量: 6
- PDF下载量: 3
- 被引次数: 0
出版历程
- 收稿日期: 2024-12-24
- 修回日期: 2025-01-28
- 录用日期: 2025-02-08
- 网络出版日期: 2025-02-25

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

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

摘要

摘要: 针对传统子空间类的波达方向估计算法对不同相干性的分布源定位时, 需要预先知道分布源的相干性问题, 文中提出一种基于深度神经网络的水下分布源波达方向估计算法。利用部分相干分布源信号模型中时间和角度相干分量的可分性, 通过分段均值归一化方法简化信号模型, 设计的深度神经网络学习不同相干系数下的数据, 提升了对不同相干性分布源波达方向角的估计能力。仿真结果说明该方法可在无相干性先验下有效估计不同相干系数下的分布源。文中方法在不同信噪比(SNR)、不同快拍数、不同相干系数情况下与4种传统子空间类方法及1种深度卷积神经网络分布源波达方向估计方法的均方根误差结果进行了对比。结果表明文中方法在相干分布源, 不同信噪比和快拍数下, 均方根误差结果平均要优于其他对比方法0.42°。非相干分布源在SNR大于0和快拍数大于600时均方根误差结果平均要优于其他方法0.04°。在不同相干系数下文中方法总体要优于其他对比方法。
- 波达方向估计 /
- 分布源 /
- 目标定位 /
- 深度神经网络
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

HTML全文

图 1 一种基于DNN的水下分布源DOA估计算法模型结构图

Figure 1. The model structure diagram of a DOA estimation algorithm for underwater distributed source based on deep neural network

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图 2 DNN模型训练迭代次数与损失值的关系图

Figure 2. The relationship between the epochs and the loss value of DNN model

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图 3 非相干分布源不同SNR下均方根误差结果对比

Figure 3. The RMSE results of ID source versus different SNRs

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图 4 相干分布源不同SNR下均方根误差结果对比图

Figure 4. The RMSE results of CD source versus different SNRs

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图 5 非相干分布源不同快拍数下均方根误差结果对比图

Figure 5. The RMSE results of ID source versus different Snapshots T

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图 6 相干分布源不同快拍数下均方根误差结果对比图

Figure 6. The RMSE results of CD source versus different Snapshots T

下载: 全尺寸图片幻灯片

图 7 部分相干分布源在不同相干系数下的均方根误差结果对比图

Figure 7. The RMSE results of PCD source versus different coherence coefficient $ \alpha $

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