Overview of Improved Particle Filter Based on Integrated Navigation System

Xiao Jing Du; Cong Liu; Shao Yong Hu; Huai Jian Li

doi:doi:10.11648/j.sd.20170505.21

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Overview of Improved Particle Filter Based on Integrated Navigation System

Xiao Jing Du, Cong Liu, Shao Yong Hu, Huai Jian Li

Published in Science Discovery (Volume 5, Issue 5)

Received: 12 September 2017 Accepted: Published: 14 September 2017

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Abstract

Particle filter has some advantages in dealing with the problems of the state equation nonlinear and noise distribution non Gauss in Integrated Navigation System. The summary for improved method of particle filter would be beneficial to study the application of particle filter in integrated navigation field deeply and overcome the problems of particle degeneracy and sample impoverishment with particle filtering. The basic algorithm and theory of particle filter and the reasons of particle degeneracy are expounded and the development of particle filter at home and abroad is given, then a summary for different methods to improve the performance of particle filter (including increasing particle number, resampling technology, selecting the best importance density function, and improving particle filter based on Neural Network). Several improved methods can effectively improve the filtering performance and improve positioning accuracy, in the actual situation, according to different conditions of use to choose the appropriate method of improvement.

Published in	Science Discovery (Volume 5, Issue 5)
DOI	10.11648/j.sd.20170505.21
Page(s)	369-374
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Particle Filter Particle Degeneracy, Sample Poverty, Improved Methods

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APA Style

Xiao Jing Du, Cong Liu, Shao Yong Hu, Huai Jian Li. (2017). Overview of Improved Particle Filter Based on Integrated Navigation System. Science Discovery, 5(5), 369-374. https://doi.org/10.11648/j.sd.20170505.21

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ACS Style

Xiao Jing Du; Cong Liu; Shao Yong Hu; Huai Jian Li. Overview of Improved Particle Filter Based on Integrated Navigation System. Sci. Discov. 2017, 5(5), 369-374. doi: 10.11648/j.sd.20170505.21

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AMA Style

Xiao Jing Du, Cong Liu, Shao Yong Hu, Huai Jian Li. Overview of Improved Particle Filter Based on Integrated Navigation System. Sci Discov. 2017;5(5):369-374. doi: 10.11648/j.sd.20170505.21

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@article{10.11648/j.sd.20170505.21,
  author = {Xiao Jing Du and Cong Liu and Shao Yong Hu and Huai Jian Li},
  title = {Overview of Improved Particle Filter Based on Integrated Navigation System},
  journal = {Science Discovery},
  volume = {5},
  number = {5},
  pages = {369-374},
  doi = {10.11648/j.sd.20170505.21},
  url = {https://doi.org/10.11648/j.sd.20170505.21},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20170505.21},
  abstract = {Particle filter has some advantages in dealing with the problems of the state equation nonlinear and noise distribution non Gauss in Integrated Navigation System. The summary for improved method of particle filter would be beneficial to study the application of particle filter in integrated navigation field deeply and overcome the problems of particle degeneracy and sample impoverishment with particle filtering. The basic algorithm and theory of particle filter and the reasons of particle degeneracy are expounded and the development of particle filter at home and abroad is given, then a summary for different methods to improve the performance of particle filter (including increasing particle number, resampling technology, selecting the best importance density function, and improving particle filter based on Neural Network). Several improved methods can effectively improve the filtering performance and improve positioning accuracy, in the actual situation, according to different conditions of use to choose the appropriate method of improvement.},
 year = {2017}
}

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TY  - JOUR
T1  - Overview of Improved Particle Filter Based on Integrated Navigation System
AU  - Xiao Jing Du
AU  - Cong Liu
AU  - Shao Yong Hu
AU  - Huai Jian Li
Y1  - 2017/09/14
PY  - 2017
N1  - https://doi.org/10.11648/j.sd.20170505.21
DO  - 10.11648/j.sd.20170505.21
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 369
EP  - 374
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20170505.21
AB  - Particle filter has some advantages in dealing with the problems of the state equation nonlinear and noise distribution non Gauss in Integrated Navigation System. The summary for improved method of particle filter would be beneficial to study the application of particle filter in integrated navigation field deeply and overcome the problems of particle degeneracy and sample impoverishment with particle filtering. The basic algorithm and theory of particle filter and the reasons of particle degeneracy are expounded and the development of particle filter at home and abroad is given, then a summary for different methods to improve the performance of particle filter (including increasing particle number, resampling technology, selecting the best importance density function, and improving particle filter based on Neural Network). Several improved methods can effectively improve the filtering performance and improve positioning accuracy, in the actual situation, according to different conditions of use to choose the appropriate method of improvement.
VL  - 5
IS  - 5
ER  -

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Author Information

Xiao Jing Du

School of Aerospace Engineering, Beijing Institute of Technology, Beijing, China
Cong Liu

School of Aerospace Engineering, Beijing Institute of Technology, Beijing, China
Shao Yong Hu

School of Aerospace Engineering, Beijing Institute of Technology, Beijing, China
Huai Jian Li

School of Aerospace Engineering, Beijing Institute of Technology, Beijing, China

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Plain Text BibTeX RIS

APA Style

Xiao Jing Du, Cong Liu, Shao Yong Hu, Huai Jian Li. (2017). Overview of Improved Particle Filter Based on Integrated Navigation System. Science Discovery, 5(5), 369-374. https://doi.org/10.11648/j.sd.20170505.21

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ACS Style

Xiao Jing Du; Cong Liu; Shao Yong Hu; Huai Jian Li. Overview of Improved Particle Filter Based on Integrated Navigation System. Sci. Discov. 2017, 5(5), 369-374. doi: 10.11648/j.sd.20170505.21

Copy | Download

AMA Style

Xiao Jing Du, Cong Liu, Shao Yong Hu, Huai Jian Li. Overview of Improved Particle Filter Based on Integrated Navigation System. Sci Discov. 2017;5(5):369-374. doi: 10.11648/j.sd.20170505.21

Copy | Download

@article{10.11648/j.sd.20170505.21,
  author = {Xiao Jing Du and Cong Liu and Shao Yong Hu and Huai Jian Li},
  title = {Overview of Improved Particle Filter Based on Integrated Navigation System},
  journal = {Science Discovery},
  volume = {5},
  number = {5},
  pages = {369-374},
  doi = {10.11648/j.sd.20170505.21},
  url = {https://doi.org/10.11648/j.sd.20170505.21},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20170505.21},
  abstract = {Particle filter has some advantages in dealing with the problems of the state equation nonlinear and noise distribution non Gauss in Integrated Navigation System. The summary for improved method of particle filter would be beneficial to study the application of particle filter in integrated navigation field deeply and overcome the problems of particle degeneracy and sample impoverishment with particle filtering. The basic algorithm and theory of particle filter and the reasons of particle degeneracy are expounded and the development of particle filter at home and abroad is given, then a summary for different methods to improve the performance of particle filter (including increasing particle number, resampling technology, selecting the best importance density function, and improving particle filter based on Neural Network). Several improved methods can effectively improve the filtering performance and improve positioning accuracy, in the actual situation, according to different conditions of use to choose the appropriate method of improvement.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Overview of Improved Particle Filter Based on Integrated Navigation System
AU  - Xiao Jing Du
AU  - Cong Liu
AU  - Shao Yong Hu
AU  - Huai Jian Li
Y1  - 2017/09/14
PY  - 2017
N1  - https://doi.org/10.11648/j.sd.20170505.21
DO  - 10.11648/j.sd.20170505.21
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 369
EP  - 374
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20170505.21
AB  - Particle filter has some advantages in dealing with the problems of the state equation nonlinear and noise distribution non Gauss in Integrated Navigation System. The summary for improved method of particle filter would be beneficial to study the application of particle filter in integrated navigation field deeply and overcome the problems of particle degeneracy and sample impoverishment with particle filtering. The basic algorithm and theory of particle filter and the reasons of particle degeneracy are expounded and the development of particle filter at home and abroad is given, then a summary for different methods to improve the performance of particle filter (including increasing particle number, resampling technology, selecting the best importance density function, and improving particle filter based on Neural Network). Several improved methods can effectively improve the filtering performance and improve positioning accuracy, in the actual situation, according to different conditions of use to choose the appropriate method of improvement.
VL  - 5
IS  - 5
ER  -

Copy | Download