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在GBSAR监测中使用Sage-Husa自适应卡尔曼过和RTS平滑进行检查模式的数据重建方法

Yaolong Qi^1,2, Jialiang Guo^1,2, Jiaxin Hui^1,2

¹College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China.

Sensors (Basel, Switzerland)

|July 12, 2025

概括

这项研究引入了一种新的方法,用于重建地面合成光圈雷达 (GBSAR) 监测中缺少的数据. 该方法结合了自适应卡尔曼过和RTS平滑来恢复空间和时间连续性,提高了变形分析的准确性.

科学领域:

地质物理学地质物理学
地质测量是指地质测量.
遥感遥感遥感遥感

背景情况:

基于地面的合成孔径雷达 (GBSAR) 对地质危险的早期预警和结构变形监测至关重要.
数据缺口,称为"检查模式",破坏GBSAR的时空连续性和变形分析准确性.
设备突然故障或多区域运行导致这些关键数据缺口.

研究的目的:

为GBSAR检查模式开发一个强大的数据重建方法.
为了解决缺少数据造成的空间和时间连续性的损失.
为了提高变形监测的准确性和稳定性.

主要方法:

一种新的数据重建方法,结合了Sage-Husa Kalman适应过和Rauch-Tung-Striebel (RTS) 平滑.
动态调整噪声共变量以适应非静态观测数据,克服传统的卡尔曼波器"模型不匹配".
使用RTS平滑来消除数据空白期间的累积错误,并恢复完整的变形时间序列.

主要成果:

提出的方法成功地恢复了GBSAR检查数据的空间和时间连续性.
实验和模拟结果证实了自适应过和光滑方法的有效性.
在变形监测的整体准确性和稳定性方面取得了显著的改进.

结论:

结合的Sage-Husa Kalman自适应过和RTS平滑有效地重建了GBSAR数据缺口.
这种方法提高了对地质危险和工程结构的变形监测的可靠性.
该方法为在连续GBSAR监控系统中维护数据完整性提供了可行的解决方案.

关键词:

在GB-SAR中使用GB-SAR.在RTS平滑过程中,RTS平滑圣人胡萨卡尔曼过器的过器数据重建数据重建检查模式检查模式

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