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在混合噪声环境中的水下声学载体DOA估计基于稀疏的专家混合机制

Wenjie Xu¹, Shichao Yi^2,3,4, Huangliang Gu⁵

¹School of Computer, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, 212003, China.

Scientific reports

|January 25, 2026

概括

这项研究引入了一种新的深度学习模型,用于在具有挑战性的水下混合噪声环境中估计到达方向 (DOA). 与传统技术相比,新方法显著提高了准确性.

科学领域:

声学和信号处理
人工智能和机器学习

背景情况:

传统的到达方向 (DOA) 估计方法在混合噪声中失败,因为它依赖于白色高斯噪声假设.
水下环境往往具有复杂的,非理想的噪声条件,降低了DOA估计性能.

研究的目的:

开发一个强大的深度神经网络用于水下DOA估计,能够处理混合噪声环境.
在复杂的声学环境中提高DOA估计的准确性和可靠性.

主要方法:

一个稀疏的专家混合 (SMoE) 深度神经网络被设计用于DOA估计.
复杂的共变矩阵被转化为实矩阵,并通过卷积神经网络 (CNN) 提取空间特征.
通过稀疏的专家激活,SMoE机制通过稀疏的专家激活来动态适应各种噪声条件.

主要成果:

拟议的SMoE模型在6种不同类型的噪声中,在0dB的信号噪声比 (SNR) 时,实现了0.94的根平均平方误差 (RMSE).
该模型在混合噪声场景中,与传统的DOA估计方法相比,表现优越.
使用分阶段训练策略来优化模型的性能.

结论:

开发的基于SMoE的深度学习方法为混合噪音中的水下DOA估计提供了可行和有效的解决方案.
这项研究提升了在复杂和不可预测的水下声环境中声学传感的能力.
SMoE机制的动态适应提供了对不同噪声特征的稳定性.

关键词:

在 DOA 估计中,估计了 DOA.深度学习是一种深度学习.混合噪声是什么?混合噪声是什么?专家的混合.信号处理信号处理

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