基于卡尔曼滤波的GM(1,1)模型在三维激光扫描边坡沉降变形分析中的应用

Application of GM(1,1) Model Based on Kalman Filter in Analysis of Slope Settlement Deformation in 3D Laser Scanning

利用三维地面激光扫描技术进行边坡变形监测时,由于其监测的数据容易受树木、行人、行车、电线杆等外界因素的干扰,所得到的结果是一组含噪声较多的边坡沉降时间序列。通过卡尔曼滤波对初始监测数据进行去噪处理,可以平滑掉曲线上波动较大的尖点,得到更合理的沉降曲线,有效提高模型的预测精度。基于以上理论,本文采用三维激光扫描仪对西北某湿陷性黄土边坡进行扫描,将得到的点云数据采用重心法提取特征点的高程坐标,再对数据采用卡尔曼滤波算法进行滤波去噪处理,通过建立基于卡尔曼滤波算法的新预测模型来预测其沉降变形。通过工程实例表明:建立的新模型的拟合和预测精度要优于传统灰色预测模型,具有较好的借鉴意义,为以后三维激光扫描技术在边坡变形监测中的应用提供指导。

When 3 D ground laser scanning technology is used to monitor slope deformation,the monitored data is easily disturbed by external factors,such as trees,pedestrians,driving and telegraph poles.The result is a set of time series of slope settlement with more noise.The Kalman filter is applied to de-noise the initial monitoring data,which can smooth out the sharp cusps on the curve and get a more reasonable settlement curve,and effectively improve the prediction accuracy of the model.Based on the above theory,this paper uses 3 D laser scanner to scan a northwest loess slope in northwest China,the point cloud data is extracted the elevation coordinates of the feature points by the center of gravity method,and then Kalman filter algorithm is used to filter and de-noise the data,a new model based on Kalman filter algorithm is established to predict the settlement deformation.The analysis results of some examples show that t the new model is better than the traditional grey prediction model in fitting and forecasting accuracy,which provides a reference for the application of 3 D laser scanning in slope deformation monitoring.