A new multi-scale sequential data fusion scheme

Researches on multi-scale data fusion have become a hot topic in data fusion field. However, limited by the constraint that signal to implement wavelet transform must have the length of 2 n , data fusion problem involved non-2 n sampled observation data still hasnpsilat been efficiently solved. In t...

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Bibliographic Details
Published in2008 International Conference on Machine Learning and Cybernetics Vol. 7; pp. 4029 - 4033
Main Authors Fu-Na Zhou, Tian-Hao Tang, Cheng-Lin Wen
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2008
Subjects
Estimation
Filtering algorithms
Filtering theory
Hybrid wavelet-Kalman filter
Kalman filters
Mathematical model
Non-2 n sample
Sequential fusion
Transforms
Wavelet transforms
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Summary:Researches on multi-scale data fusion have become a hot topic in data fusion field. However, limited by the constraint that signal to implement wavelet transform must have the length of 2 n , data fusion problem involved non-2 n sampled observation data still hasnpsilat been efficiently solved. In this paper, we aim to develop a new sequential fusion scheme by designing the stacked observation model for hybrid wavelet-Kalman filter based sequential data fusion method for the fusion of non-2 n sampled multi-sensor dynamic system by analyzing the possible observation structure of non-2 n sampled sensor. Simulation of three sensors with sampling interval 1, 2 and 3 shows the efficiency of this scheme.
ISBN:1424420954
9781424420957
ISSN:2160-133X
DOI:10.1109/ICMLC.2008.4621107