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Proceedings Paper

Improvement of strong tracking Kalman filter based on fuzzy forgetting factor
Author(s): Yong-jun Zhang; Zhi-gang Yang; Jing Wang
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Paper Abstract

In the strong tracking Kalman filter algorithm with multiple suboptimal fading factors, the optimum filter tracking performance cannot been achieved when the forgetting factor in estimation formula of state error covariance matrix takes an inappropriate value. In this paper, an estimation method of error variance matrix on the basis of fuzzy forgetting factor was proposed. Using the fuzzy logic controller to monitor fuzzy similarity coefficient and state estimation variance, this method regulates fuzzy forgetting factor according to fuzzy rules, and then adjusts suboptimal multiple fading factors to improve the tracking precision of the filter in the strong tracking Kalman filter algorithm. The simulation result proves the effectiveness of the algorithm.

Paper Details

Date Published: 13 March 2013
PDF: 7 pages
Proc. SPIE 8784, Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies, 87840T (13 March 2013); doi: 10.1117/12.2013920
Show Author Affiliations
Yong-jun Zhang, Univ. of Science and Technology Beijing (China)
Zhi-gang Yang, Univ. of Science and Technology Beijing (China)
Jing Wang, Univ. of Science and Technology Beijing (China)


Published in SPIE Proceedings Vol. 8784:
Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies
Yulin Wang; Liansheng Tan; Jianhong Zhou, Editor(s)

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