Adaptive strategy-based tightly-coupled ins/gnss integration system aided by odometer and barometer

Y. C. Tien, Y. L. Chen, Kai-Wei Chiang

Research output: Contribution to journalConference article

Abstract

Inertial Navigation System/Global Navigation Satellite System (INS/GNSS) integration system have been widely applied in recent years. Unfortunately, it sometimes malfunctions and the performance heavily deteriorates, especially in urban area where signals from satellites may be blocked or reflected by modern buildings. In multipath or Non Light-of-sight (NLOS) environment, incorrect signal results in poor observability of GNSS measurement model in Kalman Filter (KF). For purpose of addressing the issue, we proposed an adaptive strategy-based tightly-coupled INS/GNSS integration system aided by odometer and barometer, targeting to mitigate the error from poor observability. In this method, tightly-coupled (TC) scheme is implemented as the fundamental system in order to increase the reliability and stability. TC is more suitable than Loosely-coupled (LC), the traditional scheme, in urban navigation because it requires less visible GNSS measurement and it overcomes the disadvantage of LC, and further enhances the navigation result. Furthermore, aiding sensors such as odometer and barometer are integrated in this system as well, serving as velocity and height constraints respectively. Since the precision of GNSS positioning depends on the properties of the environment, measurement model of KF must work adaptively. Thus, innovation-based Adaptive Scaled Estimation (IASE) and Residual-based Adaptive Scaled Estimation (RASE), are also implemented to improve navigation performance in this paper. Finally, from the experimental validation, the proposed adaptive sensor-fusion navigation algorithm significantly enhanced the performance. The improvement was approximate 80% compared with the pure TC scheme; the RMSE can reach 6m in 3D and 2.5 m in vertical.

Original languageEnglish
Pages (from-to)881-888
Number of pages8
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Volume42
Issue number2/W13
DOIs
Publication statusPublished - 2019 Jun 4
Event4th ISPRS Geospatial Week 2019 - Enschede, Netherlands
Duration: 2019 Jun 102019 Jun 14

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Barometers
GNSS
navigation
Navigation
Inertial navigation systems
Observability
Satellites
Kalman filter
Kalman filters
sensor
Sensors
performance
positioning
targeting
Fusion reactions
innovation
urban area
Innovation
building

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Geography, Planning and Development

Cite this

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title = "Adaptive strategy-based tightly-coupled ins/gnss integration system aided by odometer and barometer",
abstract = "Inertial Navigation System/Global Navigation Satellite System (INS/GNSS) integration system have been widely applied in recent years. Unfortunately, it sometimes malfunctions and the performance heavily deteriorates, especially in urban area where signals from satellites may be blocked or reflected by modern buildings. In multipath or Non Light-of-sight (NLOS) environment, incorrect signal results in poor observability of GNSS measurement model in Kalman Filter (KF). For purpose of addressing the issue, we proposed an adaptive strategy-based tightly-coupled INS/GNSS integration system aided by odometer and barometer, targeting to mitigate the error from poor observability. In this method, tightly-coupled (TC) scheme is implemented as the fundamental system in order to increase the reliability and stability. TC is more suitable than Loosely-coupled (LC), the traditional scheme, in urban navigation because it requires less visible GNSS measurement and it overcomes the disadvantage of LC, and further enhances the navigation result. Furthermore, aiding sensors such as odometer and barometer are integrated in this system as well, serving as velocity and height constraints respectively. Since the precision of GNSS positioning depends on the properties of the environment, measurement model of KF must work adaptively. Thus, innovation-based Adaptive Scaled Estimation (IASE) and Residual-based Adaptive Scaled Estimation (RASE), are also implemented to improve navigation performance in this paper. Finally, from the experimental validation, the proposed adaptive sensor-fusion navigation algorithm significantly enhanced the performance. The improvement was approximate 80{\%} compared with the pure TC scheme; the RMSE can reach 6m in 3D and 2.5 m in vertical.",
author = "Tien, {Y. C.} and Chen, {Y. L.} and Kai-Wei Chiang",
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AU - Tien, Y. C.

AU - Chen, Y. L.

AU - Chiang, Kai-Wei

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N2 - Inertial Navigation System/Global Navigation Satellite System (INS/GNSS) integration system have been widely applied in recent years. Unfortunately, it sometimes malfunctions and the performance heavily deteriorates, especially in urban area where signals from satellites may be blocked or reflected by modern buildings. In multipath or Non Light-of-sight (NLOS) environment, incorrect signal results in poor observability of GNSS measurement model in Kalman Filter (KF). For purpose of addressing the issue, we proposed an adaptive strategy-based tightly-coupled INS/GNSS integration system aided by odometer and barometer, targeting to mitigate the error from poor observability. In this method, tightly-coupled (TC) scheme is implemented as the fundamental system in order to increase the reliability and stability. TC is more suitable than Loosely-coupled (LC), the traditional scheme, in urban navigation because it requires less visible GNSS measurement and it overcomes the disadvantage of LC, and further enhances the navigation result. Furthermore, aiding sensors such as odometer and barometer are integrated in this system as well, serving as velocity and height constraints respectively. Since the precision of GNSS positioning depends on the properties of the environment, measurement model of KF must work adaptively. Thus, innovation-based Adaptive Scaled Estimation (IASE) and Residual-based Adaptive Scaled Estimation (RASE), are also implemented to improve navigation performance in this paper. Finally, from the experimental validation, the proposed adaptive sensor-fusion navigation algorithm significantly enhanced the performance. The improvement was approximate 80% compared with the pure TC scheme; the RMSE can reach 6m in 3D and 2.5 m in vertical.

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