Research on integrated navigation technology of field robot

Feng Chun Zhu; Yan Bing Ju; Ai Hua Wang

doi:10.1109/ICIA.2006.305802

Research on integrated navigation technology of field robot

Feng Chun Zhu^*, Yan Bing Ju, Ai Hua Wang

^*Corresponding author for this work

School of Information and Electronics

Research output: Contribution to conference › Paper › peer-review

3 Citations (Scopus)

Abstract

This paper introduced GPS/INS integrated navigation technology into field robot navigation system, and mainly discussed the data fusion algorithm based on fuzzy adaptive Kalman filter. For the reason that classical Kalman filter might lead to divergence of system state parameter estimation when it dealt with time varied statistic of measurement noise in different working conditions, then by monitoring the variation grade of the actual residual compared with filter residual, the novel algorithm could adjust recursively the measurement noise covariance of Kalman filter online to make it close to real measurement covariance gradually. As a result, the Kalman filter performs optimally and the accuracy of the navigation system is improved. The simulation result also proves that this fuzzy adaptive Kalman filter works better than the conventional filtering algorithm.

Original language	English
Pages	59-64
Number of pages	6
DOIs	https://doi.org/10.1109/ICIA.2006.305802
Publication status	Published - 2006
Event	2006 IEEE International Conference on Information Acquisition, ICIA 2006 - Weihai, Shandong, China Duration: 20 Aug 2006 → 23 Aug 2006

Conference

Conference	2006 IEEE International Conference on Information Acquisition, ICIA 2006
Country/Territory	China
City	Weihai, Shandong
Period	20/08/06 → 23/08/06

Keywords

Data fusion
Fuzzy adaptive filter
Kalman filter
Navigation

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10.1109/ICIA.2006.305802

Cite this

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title = "Research on integrated navigation technology of field robot",

abstract = "This paper introduced GPS/INS integrated navigation technology into field robot navigation system, and mainly discussed the data fusion algorithm based on fuzzy adaptive Kalman filter. For the reason that classical Kalman filter might lead to divergence of system state parameter estimation when it dealt with time varied statistic of measurement noise in different working conditions, then by monitoring the variation grade of the actual residual compared with filter residual, the novel algorithm could adjust recursively the measurement noise covariance of Kalman filter online to make it close to real measurement covariance gradually. As a result, the Kalman filter performs optimally and the accuracy of the navigation system is improved. The simulation result also proves that this fuzzy adaptive Kalman filter works better than the conventional filtering algorithm.",

keywords = "Data fusion, Fuzzy adaptive filter, Kalman filter, Navigation",

author = "Zhu, {Feng Chun} and Ju, {Yan Bing} and Wang, {Ai Hua}",

year = "2006",

doi = "10.1109/ICIA.2006.305802",

language = "English",

pages = "59--64",

note = "2006 IEEE International Conference on Information Acquisition, ICIA 2006 ; Conference date: 20-08-2006 Through 23-08-2006",

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T1 - Research on integrated navigation technology of field robot

AU - Zhu, Feng Chun

AU - Ju, Yan Bing

AU - Wang, Ai Hua

PY - 2006

Y1 - 2006

N2 - This paper introduced GPS/INS integrated navigation technology into field robot navigation system, and mainly discussed the data fusion algorithm based on fuzzy adaptive Kalman filter. For the reason that classical Kalman filter might lead to divergence of system state parameter estimation when it dealt with time varied statistic of measurement noise in different working conditions, then by monitoring the variation grade of the actual residual compared with filter residual, the novel algorithm could adjust recursively the measurement noise covariance of Kalman filter online to make it close to real measurement covariance gradually. As a result, the Kalman filter performs optimally and the accuracy of the navigation system is improved. The simulation result also proves that this fuzzy adaptive Kalman filter works better than the conventional filtering algorithm.

AB - This paper introduced GPS/INS integrated navigation technology into field robot navigation system, and mainly discussed the data fusion algorithm based on fuzzy adaptive Kalman filter. For the reason that classical Kalman filter might lead to divergence of system state parameter estimation when it dealt with time varied statistic of measurement noise in different working conditions, then by monitoring the variation grade of the actual residual compared with filter residual, the novel algorithm could adjust recursively the measurement noise covariance of Kalman filter online to make it close to real measurement covariance gradually. As a result, the Kalman filter performs optimally and the accuracy of the navigation system is improved. The simulation result also proves that this fuzzy adaptive Kalman filter works better than the conventional filtering algorithm.

KW - Data fusion

KW - Fuzzy adaptive filter

KW - Kalman filter

KW - Navigation

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U2 - 10.1109/ICIA.2006.305802

DO - 10.1109/ICIA.2006.305802

M3 - Paper

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SP - 59

EP - 64

T2 - 2006 IEEE International Conference on Information Acquisition, ICIA 2006

Y2 - 20 August 2006 through 23 August 2006

ER -

Research on integrated navigation technology of field robot

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Keywords

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