A novel Gaussian particle filter based on randomized quasi Monte Carlo for initial alignment in SINS

Junhou Wang; Chunlei Song; Jiabin Chen; Zhide Liu; Xingtai Yao

doi:10.1109/ICINFA.2010.5512122

A novel Gaussian particle filter based on randomized quasi Monte Carlo for initial alignment in SINS

Junhou Wang^*, Chunlei Song, Jiabin Chen, Zhide Liu, Xingtai Yao

^*Corresponding author for this work

School of Automation

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

The error model of marine strapdown inertial navigation system on the swaying base is nonlinear, while the azimuth angle is large. For the nonlinear error model, a new recursive Gaussian Particle filter based on randomized Quasi Monte Carlo is proposed. The randomized Quasi Monte Carlo methods use the weighted randomized low discrepancy particles to replace the weighted random samples, in order to avoid the possible gaps and clusters that arise from random sampling in Monte Carlo methods, and improve the sampling efficiency and calculation accuracy. The simulation experiment shows that the new approach obtains the better estimation performance in initial alignment of large azimuth misalignment on the swaying base of the marine strapdown inertial navigation system.

Original language	English
Title of host publication	2010 IEEE International Conference on Information and Automation, ICIA 2010
Pages	1245-1250
Number of pages	6
DOIs	https://doi.org/10.1109/ICINFA.2010.5512122
Publication status	Published - 2010
Event	2010 IEEE International Conference on Information and Automation, ICIA 2010 - Harbin, Heilongjiang, China Duration: 20 Jun 2010 → 23 Jun 2010

Publication series

Name	2010 IEEE International Conference on Information and Automation, ICIA 2010

Conference

Conference	2010 IEEE International Conference on Information and Automation, ICIA 2010
Country/Territory	China
City	Harbin, Heilongjiang
Period	20/06/10 → 23/06/10

Keywords

Gaussian particle filter
Initial alignment
Randomized quasi Monte Carlo
Strapdown inertial navigation system
Swaying base

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/ICINFA.2010.5512122

Cite this

Wang, J., Song, C., Chen, J., Liu, Z., & Yao, X. (2010). A novel Gaussian particle filter based on randomized quasi Monte Carlo for initial alignment in SINS. In 2010 IEEE International Conference on Information and Automation, ICIA 2010 (pp. 1245-1250). Article 5512122 (2010 IEEE International Conference on Information and Automation, ICIA 2010). https://doi.org/10.1109/ICINFA.2010.5512122

@inproceedings{502f71cc396e49efbe80c2b914eb9fe4,

title = "A novel Gaussian particle filter based on randomized quasi Monte Carlo for initial alignment in SINS",

abstract = "The error model of marine strapdown inertial navigation system on the swaying base is nonlinear, while the azimuth angle is large. For the nonlinear error model, a new recursive Gaussian Particle filter based on randomized Quasi Monte Carlo is proposed. The randomized Quasi Monte Carlo methods use the weighted randomized low discrepancy particles to replace the weighted random samples, in order to avoid the possible gaps and clusters that arise from random sampling in Monte Carlo methods, and improve the sampling efficiency and calculation accuracy. The simulation experiment shows that the new approach obtains the better estimation performance in initial alignment of large azimuth misalignment on the swaying base of the marine strapdown inertial navigation system.",

keywords = "Gaussian particle filter, Initial alignment, Randomized quasi Monte Carlo, Strapdown inertial navigation system, Swaying base",

author = "Junhou Wang and Chunlei Song and Jiabin Chen and Zhide Liu and Xingtai Yao",

year = "2010",

doi = "10.1109/ICINFA.2010.5512122",

language = "English",

isbn = "9781424457021",

series = "2010 IEEE International Conference on Information and Automation, ICIA 2010",

pages = "1245--1250",

booktitle = "2010 IEEE International Conference on Information and Automation, ICIA 2010",

note = "2010 IEEE International Conference on Information and Automation, ICIA 2010 ; Conference date: 20-06-2010 Through 23-06-2010",

}

Wang, J, Song, C , Chen, J, Liu, Z & Yao, X 2010, A novel Gaussian particle filter based on randomized quasi Monte Carlo for initial alignment in SINS. in 2010 IEEE International Conference on Information and Automation, ICIA 2010., 5512122, 2010 IEEE International Conference on Information and Automation, ICIA 2010, pp. 1245-1250, 2010 IEEE International Conference on Information and Automation, ICIA 2010, Harbin, Heilongjiang, China, 20/06/10. https://doi.org/10.1109/ICINFA.2010.5512122

A novel Gaussian particle filter based on randomized quasi Monte Carlo for initial alignment in SINS. / Wang, Junhou; Song, Chunlei ; Chen, Jiabin et al.
2010 IEEE International Conference on Information and Automation, ICIA 2010. 2010. p. 1245-1250 5512122 (2010 IEEE International Conference on Information and Automation, ICIA 2010).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Song, Chunlei

AU - Chen, Jiabin

AU - Liu, Zhide

AU - Yao, Xingtai

PY - 2010

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N2 - The error model of marine strapdown inertial navigation system on the swaying base is nonlinear, while the azimuth angle is large. For the nonlinear error model, a new recursive Gaussian Particle filter based on randomized Quasi Monte Carlo is proposed. The randomized Quasi Monte Carlo methods use the weighted randomized low discrepancy particles to replace the weighted random samples, in order to avoid the possible gaps and clusters that arise from random sampling in Monte Carlo methods, and improve the sampling efficiency and calculation accuracy. The simulation experiment shows that the new approach obtains the better estimation performance in initial alignment of large azimuth misalignment on the swaying base of the marine strapdown inertial navigation system.

AB - The error model of marine strapdown inertial navigation system on the swaying base is nonlinear, while the azimuth angle is large. For the nonlinear error model, a new recursive Gaussian Particle filter based on randomized Quasi Monte Carlo is proposed. The randomized Quasi Monte Carlo methods use the weighted randomized low discrepancy particles to replace the weighted random samples, in order to avoid the possible gaps and clusters that arise from random sampling in Monte Carlo methods, and improve the sampling efficiency and calculation accuracy. The simulation experiment shows that the new approach obtains the better estimation performance in initial alignment of large azimuth misalignment on the swaying base of the marine strapdown inertial navigation system.

KW - Gaussian particle filter

KW - Initial alignment

KW - Randomized quasi Monte Carlo

KW - Strapdown inertial navigation system

KW - Swaying base

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DO - 10.1109/ICINFA.2010.5512122

M3 - Conference contribution

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SN - 9781424457021

T3 - 2010 IEEE International Conference on Information and Automation, ICIA 2010

SP - 1245

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BT - 2010 IEEE International Conference on Information and Automation, ICIA 2010

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Y2 - 20 June 2010 through 23 June 2010

ER -

A novel Gaussian particle filter based on randomized quasi Monte Carlo for initial alignment in SINS

Abstract

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Keywords

UN SDGs

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