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Author:

Cui, P.-Y. (Cui, P.-Y..) | Zheng, L.-F. (Zheng, L.-F..) | Pei, F.-J. (Pei, F.-J..) | Liu, H.-Y. (Liu, H.-Y..)

Indexed by:

Scopus PKU CSCD

Abstract:

Particle filter is a best way for the state estimation of highly nonlinear integrated navigation systems with non-Gaussian uncertainties. Since the error model of SINS has high dimensions, traditional particle filter would bring hard computation. A new Kalman/Particle mixed filter used on SINS/GPS integrated navigation system was proposed. The new method divides the system into two sub-models, one is linear, the other one is nonlinear, and then implement Kalman filter and particle filter separately. Residual-systematic resampling and regularized algorithms were involved to decrease particle filter' particles collapse weakness and hard computation. The simulation results show that their performance is almost equal, but the computation complexity of the Kalman/particle filter is much lower than traditional particle filter.

Keyword:

Integrated navigation system; Kalman/particle filter; Particle filter; Strap-down inertial navigation system

Author Community:

  • [ 1 ] [Cui, P.-Y.]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100022, China
  • [ 2 ] [Zheng, L.-F.]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100022, China
  • [ 3 ] [Pei, F.-J.]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100022, China
  • [ 4 ] [Liu, H.-Y.]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100022, China

Reprint Author's Address:

  • [Cui, P.-Y.]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100022, China

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Source :

Journal of System Simulation

ISSN: 1004-731X

Year: 2009

Issue: 1

Volume: 21

Page: 220-223

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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