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基于大规模监测数据的内陆水道浮标运动的时间频率特征提取和分析
Xin Li1, Yimei Chen1, Lilei Mao2
1Department of Port, Waterway and Coastal Engineering, School of Transportation, Southeast University, Nanjing 210096, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
这项研究引入了一种用于内陆水道浮标传感器数据的新型数据处理框架,通过解决噪音和不规则采样来提高准确性. 调查结果显示,浮标运动主要受到潮动力学的影响,而排水和事故的影响也很大.
科学领域:
- 地质物理学 地质物理学
- 海洋学 海洋学 海洋学
- 信号处理 信号处理
背景情况:
- 内水路浮标使用传感器进行位置监测,但数据质量受到噪音,异常值和不规则采样的影响.
- 现有的数据处理方法经常与水生环境中浮标运动的复杂动态作斗争.
研究的目的:
- 开发和验证标准化数据处理框架,以提高内陆水道浮标传感器数据的质量和可解释性.
- 确定影响浮标运动的关键因素,包括潮力,河流排水和潜在事故.
主要方法:
- 一种混合异常值检测方法,结合了四分区间范围过和隔离森林算法.
- 适应性插值使用立方线为短间隙和主导周期性估计与功率光谱密度 (PSD) 较长的间隙.
- 具有Singer运动模型的自适应性无气味卡尔曼波器 (AUKF) 用于消除噪声和动态噪声调节.
- 提取时间频率特征,如中心性,定向分散和波形变换特征.
- 在江下游使用动态时间扭曲进行浮标选择的案例研究.
主要成果:
- 拟议的框架有效地过噪音,并处理浮标数据中不规则的采样间隔.
- 浮标运动分析显示,与半日潮动态有很强的相关性.
- 河流流失和意外事件被确定为对浮标轨迹的重大次要影响.
结论:
- 开发的数据处理框架显著提高了浮标运动数据的质量和可解释性.
- 这些发现提供了对影响内陆水道浮标的水力动力学力和外部因素的更深入的了解.
- 这些增强的数据有助于建立更强大的监测系统,并在内陆水道进行精确的水力动力学分析.
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