改进重采样粒子滤波算法在GPS中的应用  

Application of improved particle filter in GPS system

作　　者：李子昱[1] 秦红磊[1] 

机构地区：[1]北京航空航天大学电子信息工程学院,北京100191

出　　处：《计算机工程与设计》2010年第11期2523-2526,共4页Computer Engineering and Design

摘　　要：为解决粒子滤波(PF)固有的退化现象及因简单重采样引起的粒子匮乏问题,采用扩展卡尔曼滤波(EKF)来优选PF的重要性分布,并对重采样方法进行改进。通过理论分析及针对全球定位系统(GPS)的计算机仿真,对比扩展卡尔曼滤波(EKF)、扩展卡尔曼粒子滤波(EKPF)以及改进的EKPF算法来实现导航定位的定位估计精度与效率,分析在不同条件状况下的最佳非线性滤波算法。实验结果表明,与其它方法相比,该算法在高动态、高机动状态下性能得到了明显的改善。To solve the degeneracy phenomenon of Particle filter（PF） and sample impoverishment problem caused by simple random resampling,based on the extended Kalman particle filter（EKPF） which selects the importance distribution of PF by the extended Kalman filter（EKF）,we improved the resampling method for the EKPF.According to theoretical analysis and computer simulation in positioning and navigation in the global positioning system（GPS）,we compared the positioning accuracy and efficiency from EKF,EKPF and the improved EKPF to give an evaluation for these algorithms in different statuses.The simulation results demonstrate that the performance of the proposed method has obvious improvement compared to other methods at the status of high dynamic,high mobility.

关 键 词：非线性滤波 扩展卡尔曼滤波 粒子滤波 重采样 全球定位系统 

分 类 号：TN967.1[电子电信—信号与信息处理]