自适应渐消Kalman滤波算法在RTK中的应用研究

An Improved Method to Solve the Floating Resolution of RTK

在动态载波相位差分定位(RTK)中,由于观测环境复杂,会经常发生周跳、卫星信号失锁等情况,严重影响基线解算的连续性和可靠性。针对动态应用环境,提出了一种Kalman滤波算法在RTK技术中的应用方法。该方法可以实时估计模糊度浮点解及其协方差矩阵,在需要重新固定模糊度时可直接用于搜索,起到了周跳修复的作用。此外,采用了自适应渐消Kalman滤波算法提高算法的动态适应性,并引入独立的滑动窗进行新息的收集和处理,解决了由于参考星变化或卫星信号失锁造成观测量中断而无法准确计算新息协方差的难题。仿真结果表明,该算法能够在模糊度发生变化时快速收敛,并且相对于一般Kalman滤波算法在高动态下提高了模糊度浮点解的精度,提高了后续模糊度搜索的效率和固定成功率。

In dynamical carrier phase differential positioning（RTK）,due to the complexity of observing environment,things like cycle slip and losing lock to satellite signal often occur,which would seriously affects the continuity and reliability of baseline solution.For the dynamical application environment,a method of Kalman filtering algorithm applied in RTK technology is proposed.This method can estimate ambiguity float solution and its covariance matrix in real time,and can be directly used for searching when ambiguities need to be refixed,and in this way the cycle slip can be repaired.Besides,the adaptive fading Kalman filter algorithm is used to improve its dynamic adaptability,and the independent sliding windows is introduced to collect and process the innovations,so as to solve the problem of calculating the covariance matrix of the innovations caused by the change of the reference satellite or the mismatching of the satellite signals resulting in the interruption of the observation.The simulation results show that the algorithm can converge quickly when the ambiguities change,improve the precision of the ambiguity float solution in high dynamic use compared with the general Kalman filtering algorithm and increase the efficiency and the fixing success rate of the following ambiguity search.