一种基于改进粒子滤波的动态精密单点定位算法

An Algorithm of Dynamic Precise Point Positioning Based on Modified Particle Filtering

提出一种改进的粒子滤波,试图提高粒子滤波的收敛速度,减弱非线性模型线性化误差和非正态分布随机误差对动态单点定位结果的影响。首先固定单差无电离层模糊度,以减少状态参数向量的维数,提高初始定位的精度和粒子滤波的收敛速度;采用Kalman滤波作为粒子滤波的预滤波,以提高粒子滤波的重点采样效率,并提高采样粒子精度,减缓粒子退化。利用一个实测动态GPS数据验证表明,改进的粒子滤波可以提高动态GPS的定位精度。

A modified particle filtering is proposed.The convergence speed of the particle filtering is tried to be improved.The influences of linearization of nonlinear functional models and the non-Gaussian random errors to the results of dynamic precise point positioning will be weakened.In the new procedure,the free-ionosphere ambiguities are fixed at first to reduce the number of parameters in the state vector.The accuracy of the initial positioning results is improved and the convergence of the particle filtering is modified.Kalman filtering as predicted filtering of particle filtering is employed to improve the efficiency of the important sampling of the particle filtering and the precision of the sampling particles,as well as to slow down the degeneracy of the particle.An actual dynamic GPS data set is employed to test the new particle filtering procedure.It is shown that the modified procedure of the particle filtering based on fixing free-ionosphere ambiguities can improve the accuracy of the dynamic precise point positioning.