Study of Surface Movement and Deformation Prediction Based [JZ]on Adaptive Kalman Filtering
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摘要: 构建自适应卡尔曼滤波预报模型,利用GNSS CORS连续运行实时监测数据,通过自适应卡尔曼滤波预报值、标准卡尔曼滤波预报值及实测数据对比分析，得到自适应卡尔曼滤波预报偏差明显减小,预报精度明显提升,满足了地表移动变形实时监测的精度要求。Abstract: Construct a variance compensation adaptive Kalman filter forecasting model, using GNSS CORS to continuously run realtime monitoring data, and compare and analyze the predicted Kalman filtering value, the standard Kalman filter forecast value and the measured data, and obtain the adaptive Kalman filter forecasting bias. Obviously reduced, the accuracy of forecasting has been significantly improved, which meets the accuracy requirements for real-time monitoring of surface movement deformation.
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