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Multi-source navigation system information fusion method based on reduced-dimensional dual quaternion

Journal Article


Abstract


  • © 2019, Editorial Department of Journal of Chinese Inertial Technology. All right reserved. Based on the research background of land vehicles, a multi-source navigation system information fusion method based on reduced-dimensional dual quaternion is proposed. Taking strap down inertial navigation system (SINS) as the public reference system, the federated Kalman filter with dimensionality reduction for SINS/GPS/geomagnetism is constructed based on the derivation of thrust velocity, gravitational velocity and position dual quaternion error equations. On the one hand, singular value decomposition (SVD) method is used to implement the observability of the system state analysis in combination with the four motion states of the vehicles: uniform velocity, acceleration, turning and climbing. On the other hand, the hierarchical designs of INS/GPS and INS/geomagnetic subfilters in the dual quaternion navigation frame are conducted under the constructed navigation frameworks, in order to obtain the optimal system state dimensionality and navigation solution efficiency. Simulation and experimental results show that the computation amount of the integrated navigation system after dimension reduction is only 3.34% of that with full state model, which leads to a better "concentration of solutions" for the parameter estimation results.

UOW Authors


  •   Xia, Linlin (external author)
  •   Xiao, Jianlei (external author)
  •   Xu, Xun
  •   Li, Xinying (external author)

Publication Date


  • 2019

Citation


  • Xia, L., Xiao, J., Xu, X. & Li, X. (2019). Multi-source navigation system information fusion method based on reduced-dimensional dual quaternion. Zhongguo Guanxing Jishu Xuebao/Journal of Chinese Inertial Technology, 27 (6), 738-745.

Scopus Eid


  • 2-s2.0-85080098669

Ro Metadata Url


  • http://ro.uow.edu.au/aiimpapers/4052

Number Of Pages


  • 7

Start Page


  • 738

End Page


  • 745

Volume


  • 27

Issue


  • 6

Place Of Publication


  • China

Abstract


  • © 2019, Editorial Department of Journal of Chinese Inertial Technology. All right reserved. Based on the research background of land vehicles, a multi-source navigation system information fusion method based on reduced-dimensional dual quaternion is proposed. Taking strap down inertial navigation system (SINS) as the public reference system, the federated Kalman filter with dimensionality reduction for SINS/GPS/geomagnetism is constructed based on the derivation of thrust velocity, gravitational velocity and position dual quaternion error equations. On the one hand, singular value decomposition (SVD) method is used to implement the observability of the system state analysis in combination with the four motion states of the vehicles: uniform velocity, acceleration, turning and climbing. On the other hand, the hierarchical designs of INS/GPS and INS/geomagnetic subfilters in the dual quaternion navigation frame are conducted under the constructed navigation frameworks, in order to obtain the optimal system state dimensionality and navigation solution efficiency. Simulation and experimental results show that the computation amount of the integrated navigation system after dimension reduction is only 3.34% of that with full state model, which leads to a better "concentration of solutions" for the parameter estimation results.

UOW Authors


  •   Xia, Linlin (external author)
  •   Xiao, Jianlei (external author)
  •   Xu, Xun
  •   Li, Xinying (external author)

Publication Date


  • 2019

Citation


  • Xia, L., Xiao, J., Xu, X. & Li, X. (2019). Multi-source navigation system information fusion method based on reduced-dimensional dual quaternion. Zhongguo Guanxing Jishu Xuebao/Journal of Chinese Inertial Technology, 27 (6), 738-745.

Scopus Eid


  • 2-s2.0-85080098669

Ro Metadata Url


  • http://ro.uow.edu.au/aiimpapers/4052

Number Of Pages


  • 7

Start Page


  • 738

End Page


  • 745

Volume


  • 27

Issue


  • 6

Place Of Publication


  • China