中长基线连续运行参考站间模糊度快速解算

A Kalman Filtering Algorithm for Ambiguity-resolving between Long-range Continuously Operating Reference Stations

为了削弱站间星间双差电离层延迟和对流层延迟,实现中长基线参考站间模糊度快速解算,对流层干延迟用模型改正;双差对流层湿延迟和电离层延迟用一阶高斯马尔科夫过程估计;在此基础上提出了双差对流层湿延迟参数、双差电离层延迟参数和模糊度作为状态向量的卡尔曼滤波算法,并结合改进的最小二乘模糊度降相关法(MLAMBDA)搜索模糊度;经验证,该算法稳定性好,初始历元数少,高度角大于20°时搜索成功率高于96%。

In order to resolve the ambiguities for long-range baselines between continuously operating reference stations and simultaneously weaken the significant residual of tropospheric delay and ionospheric delay after double-difference techniques are taken , the dry tropospheric delay is computed with a model while the double-difference tropospheric wet delay and the double-difference ionospheric delay is estimated on the manner of a first-order gauss-markov process ; then a kalman filter algorithm is proposed , which double-difference tropospheric wet delay , double-difference ionospheric delay and ambiguities as the state variables are estimated ; a Modified Least-square AMBiguity Decorrelation Adjustment ( MLAMBDA ) method is used to directly fixed double-difference ambiguities in real-time.The experimental results show that , the proposed algorithm can improve the efficiency of ambiguity resolution and only needs a few of epochs to initialize ; the success rate of resolving ambiguities is more than 96% with available satellites above the horizon ( 20° ) .