A new multi-scale sequential data fusion scheme

Author

Zhou, Fu-Na ; Tang, Tian-Hao ; Wen, Cheng-lin

Author_Institution

Logistis Sch., Shanghai Marintime Univ., Shanghai

Volume

7

fYear

2008

fDate

12-15 July 2008

Firstpage

4029

Lastpage

4033

Abstract

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 2ⁿ, data fusion problem involved non-2ⁿ 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-2ⁿ sampled multi-sensor dynamic system by analyzing the possible observation structure of non-2ⁿ sampled sensor. Simulation of three sensors with sampling interval 1, 2 and 3 shows the efficiency of this scheme.

Keywords

Kalman filters; sensor fusion; wavelet transforms; hybrid wavelet-Kalman filter; multiscale sequential data fusion scheme; non2ⁿ sampled multisensor dynamic system; stacked observation model; Cybernetics; Filters; Gaussian noise; Machine learning; Recursive estimation; Sampling methods; Sensor fusion; Sensor systems; Wavelet analysis; Wavelet transforms; Hybrid wavelet-Kalman filter; Non-2ⁿ sample; Sequential fusion;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2008 International Conference on

Conference_Location

Kunming

Print_ISBN

978-1-4244-2095-7

Electronic_ISBN

978-1-4244-2096-4

Type

conf

DOI

10.1109/ICMLC.2008.4621107

Filename

4621107