抗差自适应卡尔曼滤波模型及其在塌陷区监测中的应用

针对塌陷区等地表快速沉降区域的动力学特点及观测向量中存在的粗差对卡尔曼滤波结果的影响，设计一种抗差自适应卡尔曼滤波模型。该模型能识别稳定沉降与快速沉降2种状态，通过抗差估计减小观测向量中粗差的影响，并采用自适应因子调整动力学模型，减少状态模型的误差，提高滤波结果的精度。将该模型应用于某矿区沉降监测数据的处理，结果表明，其效果优于抗差卡尔曼滤波。

Adaptive Robust Kalman Filtering Model and Its Application in Subsidence Area Monitoring

We design a robust adaptive Kalman filter model to deal with the dynamic characteristics of rapid subsidence areas and the influence of gross errors in observation vectors on Kalman filter results. The model can identify two states of steady settlement and rapid settlement. Robust estimation is used to reduce the influence of gross errors in the observation vectors. In order to reduce the errors of the state model, an adaptive factor is used to adjust the dynamic model to improve the accuracy of the filtering results. This model is applied to the data processing of subsidence monitoring in a mining area for verification. Compared with the results of robust Kalman filter, the conclusion shows that the filtering model is better.