摘要
多径效应引起的误差是高精度卫星导航系统的定位系统的主要误差源之一。接收到的导航信号受到各种各样的环境因素的干扰,如建筑阻隔,云层阴影等。最新的消除多路径效应的方法都是基于贝叶斯估计的粒子滤波。为保持粒子空间的多样性,解决现有粒子滤波算法在多峰值的样本空间出现的退化现象,提出了一种新的基于遗传算法的粒子滤波。对本算法进行仿真,并与扩展卡尔曼滤波算法(EKF)和索非尔流形粒子滤波Stiefel Manifold Particlefilter,SMPF进行性能比较,仿真结果表明,基于遗传算法的粒子滤波能减小导航定位误差,性能优于EKF和SMPF。
Multipath is one of the most critical sources of accuracy degradation in satellite-based navigation systems.The received navigation signals,particularly in urban environments,can be affected by blockage,shadowing,and multipath reception.Latest multipath mitigation algorithms are based on the concept of sequential Bayesian estimation.In this paper,we specifically address the problem of tracking the time-variant synchronization parameters of both the line-of-sight signal（LOSS） and its multipath replicas.An efficient implementation via genetic algorithm particle filter（GAPF） is proposed,in which a particle filter and genetic algorithm are suitably combined to mitigate the multipath channel by efficiently estimating its time-variant parameters.Genetic algorithm provides an important guiding ideology by simulating the evolution mechanism for the particle degeneracy phenomenon.The performance of the GAPF is compared to those of the extended Kalman filter and the Stiefel Manifold particle filter and the simulation results show that the new particle filter is superior to the standard particle filter and the other filters.
出处
《电子测量技术》
2012年第2期50-54,共5页
Electronic Measurement Technology
