在离轴ICOS中,以NRBO-SVMD和LSTM优化的卡尔曼波器为基础,抑制空腔模式噪声
Optics express
|December 19, 2025
概括
一个新的NRBO-SVMD-LSTM-KF算法通过提高信号质量来增强OA-ICOS中的微量气体检测. 这种方法有效地抑制噪音,提高系统稳定性和检测残留空腔模式噪音的准确性.
科学领域:
- 频谱学是一种光谱学.
- 环境监测 环境监测
- 信号处理 信号处理
背景情况:
- 剩余腔模式噪声限制检测准确度在离轴集成腔输出光谱学 (OA-ICOS).
- 卡尔曼波器 (KF) 用于噪声抑制,但受到信号质量和预测准确度的限制.
研究的目的:
- 提出一个级联无声化架构,以增强OA-ICOS的观测和预测信号.
- 为了提高卡尔曼过器在抑制余腔模式噪声方面的性能.
主要方法:
- 利用基于牛顿-拉普森的优化器 (NRBO) 与顺序变化模式分解 (SVMD) 来改善观测信号.
- 采用长期短期记忆 (LSTM) 网络来提高预测信号的准确性.
- 集成优化信号使用适应加权卡尔曼波器 (KF) 进行最佳状态估计.
主要成果:
- 实现了信号噪声比 (SNR) 的显著提高,从21.159增加到33.715.
- 标准偏差 (STD) 从9.89576降至0.99406,峰值至峰值值降至6ppb.
- 显示了91.43%的信号波动抑制,改善了系统稳定性.
结论:
- 该NRBO-SVMD-LSTM-KF算法为OA-ICOS系统提供了卓越的噪声抑制.
- 这种方法为提高微量气体检测精度提供了有效的解决方案.
- 拟议的方法显著提高了系统稳定性和信号质量.
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