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Automatic estimation of inertial navigation system errors for global positioning system outage recovery

This article presents an alternative approach of solving global positioning system (GPS) outages without requiring any prior information about the characteristics of the inertial navigation system (INS) and GPS sensors. INS can be used as a standalone system to bridge the outages during GPS signal l...

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Main Authors: Hasan, Ahmed Mudheher, Samsudin, Khairulmizam, Ramli, Abdul Rahman, Raja Abdullah, Raja Syamsul Azmir
Format: Article
Language:English
Published: Institution of Mechanical Engineers 2011
Online Access:http://psasir.upm.edu.my/id/eprint/22884/1/Automatic%20estimation%20of%20inertial%20navigation%20system%20errors%20for%20global%20positioning%20system%20outage%20recovery.pdf
http://psasir.upm.edu.my/id/eprint/22884/
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spelling my.upm.eprints.228842016-09-29T05:10:39Z http://psasir.upm.edu.my/id/eprint/22884/ Automatic estimation of inertial navigation system errors for global positioning system outage recovery Hasan, Ahmed Mudheher Samsudin, Khairulmizam Ramli, Abdul Rahman Raja Abdullah, Raja Syamsul Azmir This article presents an alternative approach of solving global positioning system (GPS) outages without requiring any prior information about the characteristics of the inertial navigation system (INS) and GPS sensors. INS can be used as a standalone system to bridge the outages during GPS signal loss. Kalman filter (KF) is widely used in INS and GPS integration to present a forceful navigation solution by overcoming the GPS outage problems. Unfortunately, KF is usually criticized for working under predefined models and for its observability problem of hidden state variables, sensor dependency, and linearization dependency. This approach utilizes a genetic neuro-fuzzy system (GANFIS) to predict the INS position and velocity errors during GPS signal blockages suitable for real-time application. The proposed model is able to deal with noise and disturbances in the GPS and INS output data in different dynamic environments compared to other traditional filtering algorithms such as the neural network and neuro fuzzy. Real field test results using the micro-electro-mechanical system grade inertial measurement unit with an integrated GPS shows a significant improvement obtained from the integrated GPS/INS system using the GANFIS module compared to traditional methods such as Kalman filtering, particularly during long GPS satellite signal blockage. Institution of Mechanical Engineers 2011 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/22884/1/Automatic%20estimation%20of%20inertial%20navigation%20system%20errors%20for%20global%20positioning%20system%20outage%20recovery.pdf Hasan, Ahmed Mudheher and Samsudin, Khairulmizam and Ramli, Abdul Rahman and Raja Abdullah, Raja Syamsul Azmir (2011) Automatic estimation of inertial navigation system errors for global positioning system outage recovery. Journal of Aerospace Engineering, 225 (1). pp. 86-96. ISSN 0954-4100; ESSN: 2041-3025 10.1243/09544100JAERO731
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description This article presents an alternative approach of solving global positioning system (GPS) outages without requiring any prior information about the characteristics of the inertial navigation system (INS) and GPS sensors. INS can be used as a standalone system to bridge the outages during GPS signal loss. Kalman filter (KF) is widely used in INS and GPS integration to present a forceful navigation solution by overcoming the GPS outage problems. Unfortunately, KF is usually criticized for working under predefined models and for its observability problem of hidden state variables, sensor dependency, and linearization dependency. This approach utilizes a genetic neuro-fuzzy system (GANFIS) to predict the INS position and velocity errors during GPS signal blockages suitable for real-time application. The proposed model is able to deal with noise and disturbances in the GPS and INS output data in different dynamic environments compared to other traditional filtering algorithms such as the neural network and neuro fuzzy. Real field test results using the micro-electro-mechanical system grade inertial measurement unit with an integrated GPS shows a significant improvement obtained from the integrated GPS/INS system using the GANFIS module compared to traditional methods such as Kalman filtering, particularly during long GPS satellite signal blockage.
format Article
author Hasan, Ahmed Mudheher
Samsudin, Khairulmizam
Ramli, Abdul Rahman
Raja Abdullah, Raja Syamsul Azmir
spellingShingle Hasan, Ahmed Mudheher
Samsudin, Khairulmizam
Ramli, Abdul Rahman
Raja Abdullah, Raja Syamsul Azmir
Automatic estimation of inertial navigation system errors for global positioning system outage recovery
author_facet Hasan, Ahmed Mudheher
Samsudin, Khairulmizam
Ramli, Abdul Rahman
Raja Abdullah, Raja Syamsul Azmir
author_sort Hasan, Ahmed Mudheher
title Automatic estimation of inertial navigation system errors for global positioning system outage recovery
title_short Automatic estimation of inertial navigation system errors for global positioning system outage recovery
title_full Automatic estimation of inertial navigation system errors for global positioning system outage recovery
title_fullStr Automatic estimation of inertial navigation system errors for global positioning system outage recovery
title_full_unstemmed Automatic estimation of inertial navigation system errors for global positioning system outage recovery
title_sort automatic estimation of inertial navigation system errors for global positioning system outage recovery
publisher Institution of Mechanical Engineers
publishDate 2011
url http://psasir.upm.edu.my/id/eprint/22884/1/Automatic%20estimation%20of%20inertial%20navigation%20system%20errors%20for%20global%20positioning%20system%20outage%20recovery.pdf
http://psasir.upm.edu.my/id/eprint/22884/
_version_ 1643827912836644864
score 13.211869

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