应用高斯粒子群优化的无迹粒子滤波

doi:10.13474/j.cnki.11-2246.2017.0107

测绘通报 ›› 2017, Vol. 0 ›› Issue (4): 1-5.doi: 10.13474/j.cnki.11-2246.2017.0107

• 学术研究 • 下一篇

应用高斯粒子群优化的无迹粒子滤波

汪晶, 聂桂根, 薛长虎

武汉大学卫星导航定位技术研究中心, 湖北武汉 430079

收稿日期:2016-08-11 修回日期:2016-12-30 出版日期:2017-04-25 发布日期:2017-05-05
作者简介:汪晶(1990—),女,硕士生,主要研究方向为多源观测数据与滑坡机理模型同化理论与方法.E-mail:56183236@qq.com
基金资助:
国家重点基础研究发展计划（2013CB733205）；武汉市科技局项目（2015011701011639）

Unscented Particle Filter Using Gaussian Particle Swarm Optimization

WANG Jing, NIE Guigen, XUE Changhu

GNSS Research Center, Wuhan University, Wuhan 430079, China

Received:2016-08-11 Revised:2016-12-30 Online:2017-04-25 Published:2017-05-05

摘要/Abstract

摘要： 针对粒子滤波算法中存在的粒子退化与粒子匮乏的缺陷，提出了利用高斯粒子群优化无迹粒子滤波的新算法。算法使用无迹粒子滤波进行重要性采样，并将高斯粒子群优化算法融入重采样过程中。该算法选取的概率密度更加接近系统真实状态，有效增加了粒子的多样性，提高了抽样效率，降低了粒子退化程度，缓解了粒子匮乏现象。试验结果表明，该算法的滤波精度明显优于粒子滤波与无迹粒子滤波算法所得到的滤波精度。

关键词: 无迹粒子滤波, 高斯粒子群优化, 粒子退化, 粒子匮乏

Abstract: A new unscented particle filter using Gaussian particle swarm optimization (GPSO-UPF) algorithm is proposed in this paper to improve particle degeneracy and particle impoverishment. It uses unscented particle filter in importance sampling process and incorporates Gaussian particle swarm optimization into re-sampling process. Through GPSO-UPF, the probability density moves closely to true state, the number of effective particles and efficiency are increased, the particle degeneracy is reduced and particle impoverishment is relieved. The experimental results show that the state estimation precision of GPSO-UPF is higher than estimation precision of PF and UPF.

Key words: unscented particle filter, Gaussian particle swarm optimization, particle degeneracy, particle impoverishment

中图分类号:

P207

汪晶, 聂桂根, 薛长虎. 应用高斯粒子群优化的无迹粒子滤波[J]. 测绘通报, 2017, 0(4): 1-5.

WANG Jing, NIE Guigen, XUE Changhu. Unscented Particle Filter Using Gaussian Particle Swarm Optimization[J]. 测绘通报, 2017, 0(4): 1-5.

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应用高斯粒子群优化的无迹粒子滤波

Unscented Particle Filter Using Gaussian Particle Swarm Optimization

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