[1]张威虎,郭明香,贺元恺,等.一种改进的蝴蝶算法优化粒子滤波算法[J].西安科技大学学报,2019,(01):119-123.[doi:10.13800/j.cnki.xakjdxxb.2019.0117 ]
 ZHANG Wei-hu,GUO Ming-xiang,HE Yuan-kai,et al.An improved butterfly algorithm optimizing particle filter algorithm[J].Journal of Xi'an University of Science and Technology,2019,(01):119-123.[doi:10.13800/j.cnki.xakjdxxb.2019.0117 ]
点击复制

一种改进的蝴蝶算法优化粒子滤波算法(/HTML)
分享到:

西安科技大学学报[ISSN:1672-9315/CN:61-1434/N]

卷:
期数:
2019年01期
页码:
119-123
栏目:
出版日期:
2019-02-28

文章信息/Info

Title:
An improved butterfly algorithm optimizing particle filter algorithm
文章编号:
1672-9315(2019)01-0119-05
作者:
张威虎郭明香贺元恺孙小婷朱代先
(西安科技大学 通信与信息工程学院,陕西 西安 710054)
Author(s):
ZHANG Wei-huGUO Ming-xiangHE Yuan-kaiSUN Xiao-tingZHU Dai-xian
(College of Communication and Information Engineering,Xi'an University of Science and Technology,Xi'an 710054,China)
关键词:
粒子滤波 粒子贫化 状态估计 蝴蝶算法 滤波精度
Keywords:
particle filtering particle depletion state estimation butterfly algorithm filtering accuracy
分类号:
TP 391.4
DOI:
10.13800/j.cnki.xakjdxxb.2019.0117
文献标志码:
A
摘要:
传统的粒子滤波算法在重采样期间丢弃小重量粒子,因此重要性权重落在极少数粒子上。这会导致采样粒子贫化、粒子多样性缺失以及需要大量粒子才能进行比较准确的状态估计等问题,针对这些问题,提出了一种改进的蝶式算法优化粒子滤波算法。首先,将最新时刻观测信息引入蝴蝶香味公式中,以提高滤波精度; 其次,引入吸引半径参数来控制蝴蝶种群寻优的搜索范围,降低算法的复杂度,进而提高算法的实时性; 最后,将改进的蝴蝶种群位置更新公式用于优化迭代更新。实验结果表明,与经典粒子滤波器和现有蝶形优化算法相比,改进算法具有更低的均方误差和运行时间。并且在粒子数较少的情况下,可以实现更准确的状态估计,并改善传统滤波器的粒子耗尽现象,保证了粒子多样性。
Abstract:
Traditional particle filtering algorithms discard small weight particles during resampling,so importance weights fall on very few particles,leading to the problem of sampled particle depletion,lack of particle diversity,and the need for a large number of particles for more accurate state estimation.In this paper,an improved butterfly algorithm is proposed to optimize the particle filter algorithm.Firstly,the latest moment observation information is introduced into the butterfly flavor formula to improve the filtering accuracy; Secondly,the attraction radius parameter is introduced to control the search range of butterfly population optimization,which reduces the complexity of the algorithm and improves the real-time performance of the algorithm.Finally,the improved butterfly population location update formula is used to optimize iterative updates.The experimental results show that compared with the classical particle filter and the existing butterfly optimization algorithm,the improved algorithm has a lower mean square error and running time.And in the case of a small number of particles,more accurate state estimation can be achieved,and the particle depletion phenomenon of the conventional filter can be improved,and the particle diversity is ensured.

参考文献/References:


[1] 王法胜,鲁明羽,赵清杰,等.粒子滤波算法[J].计算机学报,2014,37(8):1679-1693. WANG Fa-sheng,LU Ming-yu,ZHAO Qing-jie,et al.Particle filtering algorithm[J].Journal of Computer,2014,37(8):1679-1693.
[2]曹 洁,荆银银,王进花.基于改进的萤火虫算法优化粒子滤波方法[J].兰州理工大学学报,2018,44(4):84-89. CAO Jie,JING Yin-yin,WANG Jin-hua.Optimized particle filter algorithm based on improved firefly algorithm[J].Journal of Lanzhou University of Technology,2018,44(4):84-89.
[3]Grodon N J,Slamond D J,Smith A F M.Novel approach to nonlinear/non-Gaussian Bayesian state estimation[J].Radar and Signal Processing,1993,140(2):107-113.
[4]Li T,Bolic M,Djuric P M.Resampling methods for particle filtering:classification,implementation,and strategies[J].IEEE Signal Processing Magazine,2015,32(3):70-86.
[5]史卓瑛.面向空旷场景基子移动设备的室内定位与导航系统[D].杭州:浙江大学,2018. SHI Zhuo-ying.Indoor localization and navigation system for mobile devices in spacious environment[D].Hangzhou:Zhejiang University,2018.
[6]张 琪,胡昌华,乔玉坤.基于权值选择的粒子滤波研究[J].控制与决策,2008,23(1):117-120. ZHANG Qi,HU Chang-hua,QIAO Yu-kun.Particle filter algorithm based on weight selected[J].Control and Decision,2008,23(1):117-120.
[7]韩 锟,张 赫.基于鸽群优化改进的粒子滤波算法[J].传感器与微系统,2018,37(11):139-144. HAN Kun,ZHANG He.Improved PF algorithm based on PIO[J].Sensors and Microsystems,2018,37(11):139-144.
[8]梁 楠,岳鹏飞,乔彦超.基于粒子群优化粒子滤波的目标跟踪方法[J].河南科学,2015,33(7):1095-1099. LIANG Nan,YUE Peng-fei,QIAO Yan-chao.Target tracking based on particle filter and particle swarm optimization[J].Henan Science,2015,33(7):1095-1099.
[9]白晓波,邵景峰,田建刚.改进的烟花算法优化粒子滤波研究[J].计算机科学与探索,2018,12(11):1827-1842. BAI Xiao-bo,SHAO Jing-feng,TIAN Jian-gang.Research on optimizing particle filter based on improved fireworks algorithm[J].Computer Science and Exploration,2018,12(11):1827-1842.
[10]陈志敏,吴盘龙,薄煜明,等.基于自控蝙蝠算法智能优化粒子滤波的机动目标跟踪方法[J].电子学报,2018,46(4):886-893. CHEN Zhi-min,WU Pan-long,BO Yu-ming,et al.Adaptive control bat algorithm intelligent optimization particle filter for maneuvering target tracking[J].Electronic Journal,2018,46(4):886-893.
[11]田梦楚,薄煜明,陈志敏,等.萤火虫算法智能优化粒子滤波[J].自动化学报,2016,42(1):89-97. TIAN Meng-chu,BO Yu-ming,CHEN Zhi-min,et al.Firefly algorithm intelligence optimized particle filter[J].Journal of Automation,2016,42(1):89-97.
[12]刘云涛.基于蝴蝶优化的粒子滤波算法[J].信息技术与网络安全,2018,37(7):37-41. LIU Yun-tao.Optimizing particle filter algorithm using butterfly algorithm[J].Information Technology and Network Security,2018,37(7):37-41.
[13]王航星,潘 巍.基于自适应吸引半径的萤火虫算法的粒子滤波[J/OL].计算机应用研究,1-8
[2018-12-20].http://kns.cnki.net/kcms/detail/51.1196.TP.20181009.1410.024.html. WANG Hang-xing,PAN Wei.Particle filter based on firefly algorithm with adaptive attraction radius[J/OL].Application Research of Computers:1-8
[2018-12-20].http://kns.cnki.net/kcms/detail/51.1196.TP.20181009.1410.024.html.
[14]Aroras,Singhs.Butterfly algorithm with Lèvy Flights for global optimization[C]//International Conference on Signal Processing,Computing and Control.IEEE,2016:220-224.

相似文献/References:

[1]孙 弋,张笑笑.结合退火优化和遗传重采样的RBPF算法[J].西安科技大学学报,2020,(02):349.
 SUN Yi,ZHANG Xiao-xiao.RBPF algorithm based on annealing optimization and genetic resampling[J].Journal of Xi'an University of Science and Technology,2020,(01):349.

备注/Memo

备注/Memo:
收稿日期:2018-09-15 责任编辑:高 佳
基金项目:陕西省自然科学基金(2017JM6102,2016JM6086)
通信作者:张威虎(1961-),男,陕西米脂人,博士,教授,E-mail:Ydzwh@163.com
更新日期/Last Update: 2019-02-28