超高层建筑动态变形分数阶Kalman滤波提取模型OA
A fractional-order Kalman filter extraction model for dynamic deformation of super-high buildings
全球卫星导航系统(GNSS)具有无须通视,能够直接测量点的三维坐标等优点,已广泛应用于超高层建筑结构动态变形监测之中.利用GNSS对建筑物进行监测时,监测数据中会包含大量噪声,为了更加准确地提取变形信息的特征,本文利用分数阶卡尔曼滤波对数据进行处理.通过分析仿真实验数据和实际案例数据,利用相关系数、均方根误差等评价参数并与卡尔曼滤波结果进行比较,验证方法的可行性.结果表明,分数阶卡尔曼滤波相较于卡尔曼滤波模型,能够有效地提取超高层建筑变形信息.
Global navigation satellite system(GNSS)has been widely used in dynamic deformation monitoring of super-high buildings because of its advantages of direct measurement of three-dimensional(3D)coordinates without access to view.When GNSS is used to monitor buildings,the monitoring data usually contains a lot of noise.In order to extract the characteristics of deformation information more accurately,this paper used a fractional-order Kalman filter(FKF)to process the data.Through the analysis of simulation experiment data and actual case data,the correlation coefficient,root mean square error,and other evaluation parameters were compared with the Kalman filter(KF)results,so as to verify the feasibility of the method.The results show that FKF is more effective than the KF model in extracting deformation information of super-high buildings.
王凯;马晓东;蒋韬;余永明;王坚;赵鑫垚
北京城建勘测设计研究院有限责任公司,北京 100101||城市轨道交通深基坑岩土工程北京市重点实验室,北京 100101北京建筑大学 测绘与城市空间信息学院,北京 102600北京建筑大学 理学院,北京 102600北京城建勘测设计研究院有限责任公司,北京 100101北京市勘察设计研究院有限公司,北京 100038
测绘与仪器
超高层建筑卡尔曼滤波(KF)分数阶卡尔曼滤波(FKF)变形监测
super-high buildingKalman filter(KF)fractional-order Kalman filter(FKF)deformation monitoring
《北京测绘》 2024 (005)
661-666 / 6
国家自然科学基金(42274029)
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