基于CNN-LSTM模型的干扰信号抑制算法
1School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
这项研究引入了使用CNN-LSTM的深度学习算法,用于无线系统中的干扰信号抑制. 该方法有效减少干扰,提高传感器可靠性和通信质量.
科学领域:
- 信号处理
- 深度学习
- 无线通信
背景情况:
- 传感器的抗干扰能力对于测量的准确性,可靠性和稳定性至关重要.
- 复杂的环境将传感器暴露在各种干扰源中,影响性能.
- 有效的干扰抑制是改善传感器操作和通信质量的关键.
研究的目的:
- 提出一种基于CNN-LSTM的算法来抑制无线通信系统中的干扰信号.
- 通过深度学习增强传感器的防干扰能力.
- 在各种干扰场景中验证算法的有效性.
主要方法:
- 使用卷积神经网络 (CNN) 进行空间特征提取.
- 使用长期短期记忆 (LSTM) 网络来捕获时间动态特征.
- 开发了一个CNN-LSTM模型用于干扰信号预测和抑制.
主要成果:
- 与LSTM,BO-LSTM和CNN-GRU相比,CNN-LSTM算法显示出小误差和高回归匹配.
- 实验模拟证实了在各种干扰条件下算法的性能.
- 使用ITU-R P.1546和现实噪声数据集的验证证实了显著的干扰抑制.
结论:
- 拟议的CNN-LSTM算法有效地抑制干扰信号和环境噪声.
- 这种深度学习方法提高了无线通信系统和传感器的稳定性和可靠性.
- 这些发现为开发更先进,耐干扰的传感器技术提供了基础.
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