Title :
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
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;
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
DOI :
10.1109/ICMLC.2008.4621107
