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DACL-Net:一个基于注意力的双分支CNN-LSTM网络,用于DOA估计

Wenjie Xu¹, Shichao Yi^2,3

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

Sensors (Basel, Switzerland)

|January 28, 2026

概括

本研究介绍了DACL-Net,这是一个用于估计到达方向 (DOA) 的新型深度学习模型. 通过转换空间数据和使用注意力机制来更好地提取特征,DACL-Net提高了准确性.

科学领域:

信号处理信号处理
人工智能的人工智能
深度学习 (Deep Learning) 是一种深度学习.

背景情况:

深度学习方法对于到达方向 (DOA) 估计是常见的.
现有的方法往往无法优化输入特征,限制了注意力机制的精度改进.

研究的目的:

提出一个新的时空融合模型,DACL-Net,用于增强DOA估计.
通过优化输入特征和采用注意力机制来提高DOA估计的准确性.

主要方法:

一个空间分支利用共变矩阵上的二维里叶变换 (2D-FT),将其转换为一个大小光谱,其中角度显示为峰值.
带有注意模块的卷积神经网络 (CNN) 专注于这些亮点组件.
一种频谱注意力机制 (SAM) 增强了时间分支中的时间特征提取.
该模型整合了空间和时间分支,用于同时学习.

主要成果:

与现有的DOA估计算法相比,DACL-Net显示出更高的准确性.
在信号与噪声比 (SNR) 为0dB的情况下,实现了0.04°的根平均平方误差 (RMSE).
拟议的特征转换和注意力机制有效地提高了DOA估计性能.

结论:

在DOA估计准确度方面,DACL-Net提供了显著的进步.
与优化特征表示相结合的时空融合方法是有效的.
该模型为在各种信号条件下对DOA估计提供了可靠的解决方案.

关键词:

在 DOA 估计中,估计了 DOA.声学矢量阵列是一个声学矢量阵列.关注注意力注意力注意力注意力深度学习是一种深度学习.信号处理信号处理信号处理

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