In the deformation monitoring based on satellite positioning, the extraction of the effective deformation signal which needs plenty of computing resources is very important. Mobile-edge computing can provide low latency and near-edge computing agility for the deformation monitoring process. In this paper, we propose an edge computing network architecture to reduce the satellite observation time while maintaining a certain positioning accuracy. In such architecture, the state transition equation is established for monitoring, and the Kalman filter is used to reduce the error caused by the reduction of the observation time. At the same time, the method of determining the initial filter value and the filtering process are given. Through the actual monitoring of a certain section of railway track, the feasibility of the proposed method is proved.

中文翻译：

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基于GNSS信号的MEC驱动快速变形监测

在基于卫星定位的变形监测中，需要大量计算资源的有效变形信号的提取非常重要。移动边缘计算可以为变形监测过程提供低延迟和近边缘计算敏捷性。在本文中，我们提出了一种边缘计算网络架构，以减少卫星观测时间，同时保持一定的定位精度。在这样的架构中，建立状态转移方程进行监测，并使用卡尔曼滤波器来减少由于观察时间减少而引起的误差。同时给出了滤波初值的确定方法和滤波过程。通过对某段铁路轨道的实际监测，证明了该方法的可行性。