Cubature Gaussian particle filter for initial alignment of strapdown inertial navigation system

Weisheng Wu*, Chunlei Song, Junhou Wang, Zhenzhen Long

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

The error model of the initial alignment of the marine strapdown inertial navigation system on the swaying base is nonlinear, while the azimuth angle error is large. For this nonlinear model, a new nonlinear filter called as the cubature Gaussian Particle filter is proposed, which is based on the cubature Kalman filter and the Gaussian Particle filter. The cubature Kalman filter is used to calculate the estimate value and the covariance matrix in the observation update for the distribution function, which is sampled as the importance density function for the Gaussian Particle filter. The simulation results demonstrate the novel filter has better estimation performance than the unscented Kalman filter and the Gaussian Particle filter in the initial alignment.

Original languageEnglish
Title of host publicationProceedings - 2010 1st International Conference on Pervasive Computing, Signal Processing and Applications, PCSPA 2010
Pages1196-1200
Number of pages5
DOIs
Publication statusPublished - 2010
Event1st International Conference on Pervasive Computing, Signal Processing and Applications, PCSPA 2010 - Harbin, China
Duration: 17 Sept 201019 Sept 2010

Publication series

NameProceedings - 2010 1st International Conference on Pervasive Computing, Signal Processing and Applications, PCSPA 2010

Conference

Conference1st International Conference on Pervasive Computing, Signal Processing and Applications, PCSPA 2010
Country/TerritoryChina
CityHarbin
Period17/09/1019/09/10

Keywords

  • Cubature Kalman filter
  • Gaussian Particle filter
  • Initial alignment
  • Strapdown inertial navigation system

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