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双分支CNN用于到达方向和来源数量的估计
1Glasgow College, University of Electronic Science and Technology of China, Chengdu 611731, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
本研究引入了一种双分支卷积神经网络 (CNN),用于同时估计到达方向 (DOA) 和源数 (NOS). 拟议的CNN模型的性能优于传统方法,特别是在具有挑战性的低信号噪声比条件下.
科学领域:
- 信号处理 信号处理
- 机器学习 机器学习
- 阵列信号处理 阵列信号处理
背景情况:
- 传统的到达方向 (DOA) 方法往往忽略了源数 (NOS) 和DOA之间的关系.
- 这种监督限制了提取有价值的估计信息的潜力.
- 现有的算法可能会在信号噪声比 (SNR) 低,快照有限或角度距离近的场景中扎.
研究的目的:
- 提出一种新的深度学习模型,用于同时估计DOA和NOS.
- 通过整合NOS和DOA估计来解决传统方法的局限性.
- 在具有挑战性的声学环境中提高估计准确性.
主要方法:
- 开发了一个双分支的卷积神经网络 (CNN) 架构.
- 挤压和激发 (SE) 块被集成到CNN中,以增强功能表示.
- 该模型旨在处理信号并同时估计DOA和NOS.
主要成果:
- 广泛的模拟验证了拟议模型的性能.
- 与几种传统的DOA估计算法相比,CNN模型显示出更高的准确性.
- 在低SNR,有限的快照和紧密间隔的事件角度条件下观察到显著的改善.
结论:
- 拟议的双分支CNN与SE块提供了一个强大的解决方案,用于同时DOA和NOS估计.
- 这种方法有效地利用NOS和DOA之间的关系来提高性能.
- 该模型对在困难的信号条件下需要精确源定位的应用特别有希望.
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