GVMD-NLM:一种用于GNSS浮标升高时间序列的混合排斥方法,使用优化的VMD和非本地媒介过
Huanghuang Zhang1, Shengping Wang1,2, Chao Dong2,3
1School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
一个新的混合无噪声框架,GVMD-NLM,有效地消除了全球导航卫星系统 (GNSS) 浮标数据中的噪声. 这种方法可以提高沿海水道的实时海拔监测准确度.
科学领域:
- 地质测量和地质数学
- 信号处理 信号处理
- 沿海工程 沿海工程
背景情况:
- 全球导航卫星系统 (GNSS) 浮标对于沿海地区的实时海拔监测至关重要.
- 来自GNSS浮标的垂直坐标时间序列经常遭受复杂的,非静止的噪声.
- 由于经验参数设置,现有的无色化技术可能缺乏可靠性.
研究的目的:
- 开发GNSS浮标数据的自动化和可靠的拒绝框架.
- 提高沿海水道实时海拔监测的准确性.
- 为了解决现有的无声化方法在处理复杂噪声方面的局限性.
主要方法:
- 拟议的GVMD-NLM:一个混合框架,结合了通过改进的灰狼优化器 (GWO) 优化的变量模式分解 (VMD) 与非局部平均值 (NLM) 过.
- 引入了一个自适应的收因子衰变函数 (基于Sigmoid) 来自动优化VMD参数 (K和α).
- 利用样本 (SE) 来识别有效的低频信号和NLM来处理高频噪声.
主要成果:
- 在东水道码头的两个数据集上,GVMD-NLM在SSA,CEEMDAN,VMD和GWO-VMD上表现优越.
- 实现了显著的根平均平方误差 (RMSE) 减少 (高达28.87%) 和信号对噪声比 (SNR) 改进 (高达11.13%).
- 否认信号保持了高保真度,相关系数 (R) 达到0.9798.8.
结论:
- 在GVMD-NLM框架提供了一个客观和自动化的解决方案,GNSS数据 denoising.
- 这种方法为水路水力动力学研究和水位监测提供了更准确的数据基础.
- 适应性参数优化提高了VMD在杂的时间序列数据分析中的可靠性和适用性.
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