一种对人类噪声敏感的模拟传感器信号处理方法,基于改进的自适应单元频谱分析
Zhengyang Gao1, Shuangchao Ge1, Jie Li1
1National Key Laboratory for Electronic Measurement Technology, North University of China, Taiyuan 030051, China.
Sensors (Basel, Switzerland)
|March 17, 2025
概括
本研究介绍了一种使用深度学习改进传感器数据处理的自适应单一频谱分析 (ASSA) 算法. 通过自动处理噪音和干扰,ASSA提高了准确性和效率,优于现有的方法.
科学领域:
- 信号处理 信号处理
- 地质物理学 地质物理学
- 机器学习 机器学习
背景情况:
- 传感器测量容易产生复杂的噪音,阻碍信号处理.
- 单频谱分析 (SSA) 在确定分解层方面面临挑战,影响精度和速度.
研究的目的:
- 为增强传感器数据处理提出改进的自适应单一光谱分析 (ASSA) 算法.
- 解决传统SSA的局限性,包括参数调整和处理时间.
主要方法:
- 集成一个深度残余网络 (Res-Net) 来自动识别干扰.
- 开发一种使用聚类用于自适应信号分类的新型相关性检测重建方法.
- 用一个全面的干扰信号数据库来训练Res-Net.
主要成果:
- 拟议的ASSA算法在磁 (MT) 数据上实现了0.2的根平均平方误差 (RMSE).
- 与其他信号提取算法相比,ASSA在准确度上有14%的改进.
- 该方法有效地抑制背景噪声,并提取有意义的信号.
结论:
- ASSA克服了确定最佳分解层的挑战,消除了手动参数调节.
- 该算法显著提高了传感器系统的测量效率和准确性.
- ASSA显示出在各种数据处理领域广泛应用的潜力.
相关概念视频
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