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Volume 42 Issue 11
Nov.  2016
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Journal of Beijing University of Aeronautics and Astronautics  > 2016  >  42(11): 2548-2554.
HUANG Shuai, CAI Hong, DING Zhijianet al. Observability analysis for transfer alignment of inertial navigation system on moving base[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(11): 2548-2554. doi: 10.13700/j.bh.1001-5965.2015.0765(in Chinese)
Citation: HUANG Shuai, CAI Hong, DING Zhijianet al. Observability analysis for transfer alignment of inertial navigation system on moving base[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(11): 2548-2554. doi: 10.13700/j.bh.1001-5965.2015.0765(in Chinese)
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Observability analysis for transfer alignment of inertial navigation system on moving base

doi: 10.13700/j.bh.1001-5965.2015.0765

  • College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China

  • Received Date: 20 Nov 2015
  • Rev Recd Date: 15 Apr 2016
  • Publish Date: 20 Nov 2016
    Abstract
  • A novel approach that starts from the basic observability definition is used to investigate the observability of the transfer alignment system on moving base. The original nonlinear state equations are analyzed directly to eliminate errors of the model. Sufficient conditions to make alignment fully observable are analytically derived. And maneuver scheme of vehicle is designed according to it. The results show that translatory motions with varying acceleration can enhance the observability of sensor bias, fix error angle and attitude, whereas angular motions are in favor of the observability of the lever arm. Different from traditional linearization-based observability studies, this kind of analysis approach is straightforward and comprehensive, with no need of nonanalytical support from numerical simulations. Besides, it has explicit physical meanings, revealing the inherent connecting link between the observability of transfer alignment system and vehicle maneuvers, and providing new insights into the analysis and design of nonlinear systems. Finally, covariance simulations with an extended Kalman filter (EKF) are carried out to estimate the states, and the numerical results accord with the theoretical analysis.

     

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