自适应卡尔曼滤波在BDS变形监测数据处理中的应用

Application of adaptive Kalman filtering in BDS deformation monitoring data processing

针对在变形监测结果中高频噪声、粗差较多,以及普通卡尔曼滤波在模型建立不准确情况下易产生数据发散的问题,提出一种自适应卡尔曼滤波方法:在普通的卡尔曼滤波算法中增加观测噪声方差缩放因子以及参考方差动态计算窗口;并根据前期监测结果中的残差方差动态调整卡尔曼滤波中的测量误差方差阵,达到自适应卡尔曼滤波的效果。实验结果表明,该方法的滤波结果相较普通卡尔曼滤波能够剔除结果中的粗差,并且能够保留被监测物的真实位移,反应速度较普通卡尔曼滤波也有很大提高。

Aiming at the problems that there are a lot of high frequency noises and gross errors in the deformation monitoring,and traditional Kalman filter is easy to generate data divergence when the model establishment is not accurate,the paper proposed an adaptive Kalman filtering method:the observation noise variance scaling factor and the reference variance dynamic calculation window were added into traditional Kalman filter;and the measurement error variance matrix in Kalman filtering was dynamically adjusted according to the residual variance of previous monitoring result for the filtering effect of the adaptive Kalman filtering.Experimental result showed that,compared with traditional method,the proposed method could eliminate the gross errors and retain the real displacement of the measured objects with higher reflection speed.