The performance analysis of an AKF based tightly-coupled INS/GNSS sensor fusion scheme with non-holonomic constraints for land vehicular applications

Kai-Wei Chiang, Cheng An Lin, Kun Yao Peng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

INS/GNSS integration scheme can overcome the shortcoming of GNSS or INS alone to provide superior performance. The position and velocity from GNSS is an excellent external aid to update the INS with improving its long-term accuracy. AKF is based on the maximum likelihood criterion for choosing the most appropriate weight and thus to adjust Kalman gain factors online. The conventional EKF implementation suffers uncertain results while the update measurement covariance matrix R does not meet the case. The primary advantage of AKF is that the filter has less relationship with the priori statistical information because R varies with time. The innovation sequence is used to derive the measurement weights through the measurement covariance matrices, innovation-based adaptive estimation (IAE) in this study. There are two non-holonomic constraints (NHC) available for land vehicle navigation. Land vehicles will not jump off or slid on the ground under normal condition. Using these constraints, the velocity of the vehicle in the plane perpendicular to the forward direction is almost zero. EKF and AKF based tightly-coupled scheme with NHC are implemented in the study. To validate the performance of EKF and AKF based tightly-coupled INS/GNSS integration scheme with NHC, field scenarios were conducted in the downtown area of Tainan city. The data fusion of INS/GNSS/NHC can be used as stand-alone positioning tool during GNSS outages of over 1 minute. The preliminary results presented in this study illustrated that AKF based tightly-coupled INS/GNSS integration scheme can provide more stable solutions. Generally speaking, the improvement ratio of 3D positioning of proposed algorithm reach 40% compared to EKF based tightly-coupled INS/GNSS integration scheme.

Original languageEnglish
Title of host publicationInnovation for Applied Science and Technology
Pages1956-1960
Number of pages5
DOIs
Publication statusPublished - 2013 Feb 20
Event2nd International Conference on Engineering and Technology Innovation 2012, ICETI 2012 - Kaohsiung, Taiwan
Duration: 2012 Nov 22012 Nov 6

Publication series

NameApplied Mechanics and Materials
Volume284-287
ISSN (Print)1660-9336
ISSN (Electronic)1662-7482

Other

Other2nd International Conference on Engineering and Technology Innovation 2012, ICETI 2012
CountryTaiwan
CityKaohsiung
Period12-11-0212-11-06

Fingerprint

Sensors
Covariance matrix
Innovation
Data fusion
Weighing
Outages
Maximum likelihood
Navigation

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Chiang, K-W., Lin, C. A., & Peng, K. Y. (2013). The performance analysis of an AKF based tightly-coupled INS/GNSS sensor fusion scheme with non-holonomic constraints for land vehicular applications. In Innovation for Applied Science and Technology (pp. 1956-1960). (Applied Mechanics and Materials; Vol. 284-287). https://doi.org/10.4028/www.scientific.net/AMM.284-287.1956
Chiang, Kai-Wei ; Lin, Cheng An ; Peng, Kun Yao. / The performance analysis of an AKF based tightly-coupled INS/GNSS sensor fusion scheme with non-holonomic constraints for land vehicular applications. Innovation for Applied Science and Technology. 2013. pp. 1956-1960 (Applied Mechanics and Materials).
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abstract = "INS/GNSS integration scheme can overcome the shortcoming of GNSS or INS alone to provide superior performance. The position and velocity from GNSS is an excellent external aid to update the INS with improving its long-term accuracy. AKF is based on the maximum likelihood criterion for choosing the most appropriate weight and thus to adjust Kalman gain factors online. The conventional EKF implementation suffers uncertain results while the update measurement covariance matrix R does not meet the case. The primary advantage of AKF is that the filter has less relationship with the priori statistical information because R varies with time. The innovation sequence is used to derive the measurement weights through the measurement covariance matrices, innovation-based adaptive estimation (IAE) in this study. There are two non-holonomic constraints (NHC) available for land vehicle navigation. Land vehicles will not jump off or slid on the ground under normal condition. Using these constraints, the velocity of the vehicle in the plane perpendicular to the forward direction is almost zero. EKF and AKF based tightly-coupled scheme with NHC are implemented in the study. To validate the performance of EKF and AKF based tightly-coupled INS/GNSS integration scheme with NHC, field scenarios were conducted in the downtown area of Tainan city. The data fusion of INS/GNSS/NHC can be used as stand-alone positioning tool during GNSS outages of over 1 minute. The preliminary results presented in this study illustrated that AKF based tightly-coupled INS/GNSS integration scheme can provide more stable solutions. Generally speaking, the improvement ratio of 3D positioning of proposed algorithm reach 40{\%} compared to EKF based tightly-coupled INS/GNSS integration scheme.",
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Chiang, K-W, Lin, CA & Peng, KY 2013, The performance analysis of an AKF based tightly-coupled INS/GNSS sensor fusion scheme with non-holonomic constraints for land vehicular applications. in Innovation for Applied Science and Technology. Applied Mechanics and Materials, vol. 284-287, pp. 1956-1960, 2nd International Conference on Engineering and Technology Innovation 2012, ICETI 2012, Kaohsiung, Taiwan, 12-11-02. https://doi.org/10.4028/www.scientific.net/AMM.284-287.1956

The performance analysis of an AKF based tightly-coupled INS/GNSS sensor fusion scheme with non-holonomic constraints for land vehicular applications. / Chiang, Kai-Wei; Lin, Cheng An; Peng, Kun Yao.

Innovation for Applied Science and Technology. 2013. p. 1956-1960 (Applied Mechanics and Materials; Vol. 284-287).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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