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Journal of Projectiles, Rockets, Missiles and Guidance >

2010 , Vol. 30 >Issue 3: 165 - 168

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2010.03.051

CORRELATION TECHNOLOGY

Two-sensor Information Fusion Steady-state Kalman Filter Based on CI

  • HUANG Yao ,
  • ZHANG Tianqi ,
  • LIU Yanli ,
  • XIA Shufang
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  • Chongqing Key Laboratory of Signal and Information Processing, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Received date: 2009-07-30

  Online published: 2025-05-28

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Abstract

For information fusion of the two-sensor steady-state Kalman filter, there are three common weighted distributed fusion algorithms: weighted by scalar, weighted by diagonal matrices and weighted by matrices. They all need calculating the local steady-state filtering error cross covariance matrix to obtain results. The covariance intersection algorithm can get an improved valuation in the face of unknown relevance. In this article, the covariance intersection algorithm was applied to the two sensor information fusion steady-state Kalman filter, in the case of unknown cross covariance matrix; good information fusion results can be obtained. Finally, an analysis of the experiment results is made and validated.

Key words： two-sensor; steady-state Kalman filter; covariance intersection algorithm

Cite this article

HUANG Yao , ZHANG Tianqi , LIU Yanli , XIA Shufang . Two-sensor Information Fusion Steady-state Kalman Filter Based on CI[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2010 , 30(3) : 165 -168 . DOI: 10.15892/j.cnki.djzdxb.2010.03.051

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