【摘要】 为了剔除GPS边坡位移监测过程中的噪声干扰,提高监测数据的有效性,特引入随机线性卡尔曼滤波离散数学模型.以水厂铁矿GPS边坡监测数据为依据,利用该数学模型可以计算出各监测点每期变形量的滤波值和位移速度,并对各监测点下一期的变形量进行估算和预测.经实例验证,卡尔曼滤波变形量与实际变形量有较好的一致性.更多还原

【Abstract】 A discrete mathematical model based on random linear Kalman filtering is introduced to eliminate random disturbance noise in the process of GPS slope deformation monitoring and to improve the validity of monitoring data.On the base of GPS slope monitoring data in Shuichang Iron Mine,the filtering value of deformation and the velocity of displacement at each point in each stage can be calculated with the mathematical model and the slope deformation at each point in the next stage can be estimated and predicted.It is proved with an example that the deformation obtained by Kalman filtering is more approximate to the real slope deformation.更多还原