Scholarlink

<p>Displacement is an important parameter in structural health monitoring (SHM). Over the decades, the Global Navigation Satellite System (GNSS) has become a significant means of acquiring displacement, especially with the widespread adoption of high-frequency GNSS, leading to its increasing applications in SHM. However, due to the influence of measurement noise, significant errors exist in the displacements obtained from GNSS kinematic positioning. To address this issue, we propose a novel displacement extraction method aimed at extracting displacements from GNSS kinematic positioning. Specifically, we design different model set based on the natural frequencies of structures and utilize interactive multi-model Kalman filtering to extract displacements. By using the designed model set, displacements can be adaptively extracted. Furthermore, we approach the issue from the perspective of model deviations and further explore the design rules of the model set. Results from simulations and field experiments have demonstrated the effectiveness and flexibility of this method.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Adaptive displacement extraction from GNSS kinematic positioning using interactive multi-model Kalman filter

  • 期刊论文

  • 发表日期：

    2025年7月28日

查看全文
  • Nan Shen,
  • Jie Song,
  • Jinghai Xu,
  • Craig Hancock,
  • Lei Wang,
  • Liang Chen

摘要

Displacement is an important parameter in structural health monitoring (SHM). Over the decades, the Global Navigation Satellite System (GNSS) has become a significant means of acquiring displacement, especially with the widespread adoption of high-frequency GNSS, leading to its increasing applications in SHM. However, due to the influence of measurement noise, significant errors exist in the displacements obtained from GNSS kinematic positioning. To address this issue, we propose a novel displacement extraction method aimed at extracting displacements from GNSS kinematic positioning. Specifically, we design different model set based on the natural frequencies of structures and utilize interactive multi-model Kalman filtering to extract displacements. By using the designed model set, displacements can be adaptively extracted. Furthermore, we approach the issue from the perspective of model deviations and further explore the design rules of the model set. Results from simulations and field experiments have demonstrated the effectiveness and flexibility of this method.