基于差分进化改进粒子滤波的多径估计算法  

An Improved Differential Evolution-Based Particle Filter for Multipath Estimation

作　　者：邢艳君[1] 程兰[1] 任密蜂[1] 王志远[1] 谢刚[1] 

机构地区：[1]太原理工大学信息工程学院,太原030024

出　　处：《太原理工大学学报》2017年第1期110-115,共6页Journal of Taiyuan University of Technology

基　　金：国家自然科学基金项目资助:基于统计信息集的非高斯系统多目标优化控制及性能评估策略研究(61503271;61603267);山西省自然科学基金项目资助(20140210022-7)

摘　　要：多径干扰因具有位置上的不相关性、不确定性等特点,不能通过差分技术来消除,成为高精度定位的主要误差源之一。因此,估计多径参数对抑制多径误差、提高导航系统的定位精度具有重要意义。本文将多径估计问题转化为状态空间模型下的参数估计问题,并利用粒子滤波(PF)进行多径估计。同时,为了克服标准PF存在粒子枯竭、导致估计结果可能收敛到错误值的问题,提出了基于差分进化改进粒子滤波(DEPF)的多径估计算法,该算法利用差分进化(DE)算法代替PF的重采样来产生新粒子,使新粒子朝着状态真实后验概率密度分布的方向移动,避免了重采样后粒子可能收敛到局部最优值的问题。仿真结果表明,在非高斯噪声下与基于PF和EKF的多径估计算法相比,本文算法具有更好的多径估计性能。Multipath is the dominant error source for high-accuracy positioning systems since it is uncorrelated and uncertain at different location and can not be eliminated by the differential technology. To this end, it is significant for eliminating the multipath error and improving the positioning accuracy to estimate multipath parameters. In this paper, the multipath estimation problem is transformed into a parameter estimation problem, and the particle filter （PF） algo rithm is applied for multipath estimation. However, there is a drawback of particle impoverish- ment in standard PF by using re-sampling strategy, which may lead to PF converging to a wrong value. In order to solve this problem, a differential evolution （DE） algorithm instead of the resampling strategy is used to generate new particles in PF. The proposed algorithm is named as improved differential evolution-based particle filter （DEPF）. In DEPF, DE algorithm is used to promote the new particle moving towards the true post probability density distribution of the tar get state, which can avoid particle impoverishment. Simulation results show that the DEPF algorithm outperforms PF and EKF for multipath estimation in non-Gaussian noise.

关 键 词：状态估计 粒子滤波 差分进化 多径干扰 导航系统 

分 类 号：TP273.1[自动化与计算机技术—检测技术与自动化装置]