估计大气延迟参数的中长基线卡尔曼滤波算法

A medium and long baseline Kalman filter algorithm for estimating atmospheric delay parameters

为了削弱中长基线差分后大气延迟残留误差对实时动态模糊度固定和定位精度的影响,该文提出了一种估计大气延迟参数的卡尔曼滤波算法,采用相对对流层天顶延迟和相对电离层天顶延迟作为残留大气延迟误差参数,与坐标向量和模糊度向量参数一起进行卡尔曼滤波估计。通过41 km参考站数据对算法进行验证。结果表明,BDS-3(B1C/B2a)与GPS精度相当,平面精度优于1.5 cm,高程精度优于2.7 cm;BDS-3(B1C/B2a)/GPS组合平面精度优于1.1 cm,高程精度优于2.7 cm,相比BDS-3(B1C/B2a)单系统平面精度提升了23.8%,估计大气延迟参数的中长基线卡尔曼滤波方法可有效提高模糊度固定成功率,提高中长基线条件下的定位精度。

In order to weaken the influence of atmospheric delay residual error on real-time kinematic ambiguity fixation and positioning accuracy after medium and long baseline difference,a Kalman filter algorithm for estimating atmospheric delay parameters was proposed.It took the relative tropospheric zenith delay and the relative ionospheric zenith delay as the residual atmospheric delay error parameters,and performed Kalman estimation together with coordinate vector and ambiguity vector parameters.The algorithm was verified by 41 km reference station data,and the results showed that BDS-3(B1 C/B2 a)had the same accuracy as GPS,the plane accuracy was better than 1.5 cm,and the elevation accuracy was better than 2.7 cm;the BDS-3(B1 C/B2 a)/GPS combination plane accuracy was better than 1.1 cm and the elevation accuracy was better than 2.7 cm;and compared with BDS-3(B1 C/B2 A)single system,the plane accuracy was improved by 23.8%.The medium and long baseline Kalman filter method for estimating atmospheric delay parameters could effectively improve the fixed rate of ambiguity and the positioning accuracy under medium and long baseline conditions.