Scholarlink

<p>Traveling ionospheric disturbances (TIDs) may significantly change the ionospheric properties in the region, which in turn affects the radio propagation process, especially on short-wave communication, satellite navigation and positioning. TIDs detection and propagation parameter calculation are essential foundations for ionospheric disturbance monitoring and early warning. Comparisons between Cascade Mask R-CNN and the classical Mask R-CNN models in instance segmentation results were conducted using global LSTIDs and European MSTIDs data. The results indicate that Cascade Mask R-CNN outperforms Mask R-CNN in image processing accuracy and training convergence speed, with an improvement of approximately 4.7% in bounding box precision and about 3.6% in mask accuracy. The model achieved mask accuracies of 79.34% and 73.37% in the European region and globally, respectively. Subsequently, irregular disturbances were normalized using a least squares ellipse fitting method, and isolated disturbances were filtered and eliminated using filtering criteria and a nonlinear programming solver. When the filtering threshold T1 was set to 40, isolated disturbances could be effectively filtered out while retaining wave disturbance components. The method yielded TIDs propagation parameters in DTEC maps in different regions that closely matched actual results.</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

Intelligent detection of traveling ionospheric disturbances from dense GNSS TEC observations utilizing instance segmentation model Cascade Mask R-CNN

  • 期刊论文

  • 发表日期：

    2025年5月23日

查看全文
  • Xiaodong Ren,
  • Pengchong Zhao,
  • Xuan Le,
  • Linghuo Jian,
  • Ahmed Abdelaziz,
  • Xiaohong Zhang

摘要

Traveling ionospheric disturbances (TIDs) may significantly change the ionospheric properties in the region, which in turn affects the radio propagation process, especially on short-wave communication, satellite navigation and positioning. TIDs detection and propagation parameter calculation are essential foundations for ionospheric disturbance monitoring and early warning. Comparisons between Cascade Mask R-CNN and the classical Mask R-CNN models in instance segmentation results were conducted using global LSTIDs and European MSTIDs data. The results indicate that Cascade Mask R-CNN outperforms Mask R-CNN in image processing accuracy and training convergence speed, with an improvement of approximately 4.7% in bounding box precision and about 3.6% in mask accuracy. The model achieved mask accuracies of 79.34% and 73.37% in the European region and globally, respectively. Subsequently, irregular disturbances were normalized using a least squares ellipse fitting method, and isolated disturbances were filtered and eliminated using filtering criteria and a nonlinear programming solver. When the filtering threshold T1 was set to 40, isolated disturbances could be effectively filtered out while retaining wave disturbance components. The method yielded TIDs propagation parameters in DTEC maps in different regions that closely matched actual results.