动态相对定位数据后处理方法研究

Research on post-processing method of dynamic relative positioning data

在GNSS-RTK形变检测领域中,动态相对定位由于观测时间短,误差消除不够充分,导致基线出现粗差和数据大噪声现象。针对上述问题,提出了一种改进无约束平差算法,减小了系统解算误差,对基线向量中的粗差也起到一定约束作用。针对基线向量中的残余粗差,采用抗差卡尔曼滤波算法进一步消除残余粗差,得到更精确的状态估值。与传统的滑动平均滤波、小波阈值去噪、EEMD-小波联合去噪进行实测效果比较分析,在不添加硬件资源的情况下,新方法联合滤波去噪效果突出,且保留了基线坐标域较多细节部分,基线的均方根误差从2.36cm减小到1.00cm,精度提升57.6%。

In the field of GNSS-RTK deformation detection,the observation time of dynamic relative positioning is short and the error elimination is not sufficient,which leads to the phenomenon of gross error and large data noise in baseline.To solve the above problems,an improved unconstrained adjustment algorithm is proposed,which can reduce the error of the system solution and restrain the gross error in the baseline vector.Aiming at the residual gross errors in the baseline vector,the anti-error Kalman filter algorithm is adopted to further eliminate the residual gross errors and obtain more accurate state estimation.Compared with traditional moving average filtering,wavelet threshold denoising and EEMD-wavelet combined denoising,the new method achieves outstanding denoising effect without adding hardware resources,and retains more details in the baseline coordinate domain.The baseline root mean square error is reduced from 2.36cm to 1.00cm,and the accuracy is improved by 57.6%.