Authors:
Funa Zhou
1
and
Tianhao Tang
2
Affiliations:
1
Shanghai Marintime University; Computer&Information Engineering School, Henan University, China
;
2
Shanghai Marintime University, China
Keyword(s):
Hybrid wavelet-Kalman filter, Sequential fusion, Non- 2n sampling.
Related
Ontology
Subjects/Areas/Topics:
Adaptive Signal Processing and Control
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Informatics in Control, Automation and Robotics
;
Signal Processing, Sensors, Systems Modeling and Control
;
Time and Frequency Response
;
Time-Frequency Analysis
Abstract:
With the development of automation, multi-scale data fusion has become a hot research topic, however, limited by the constraint that signal to implement wavelet transform must have the length of 2q, multi-scale data fusion problem involved with non- 2n sampled observation data still hasn’t been efficiently solved. In this paper, we develop a hybrid wavelet-Kalman filter multiscale sequential fusion method. First, we develop the hybrid wavelet-Kalman filter multiscale estimation method which combines the advantage of wavelet and Kalman filter to obtain the real time, recursive, multiscale estimation of the dynamic system. Then, a multiscale sequential fusion method is presented. Under the hybrid wavelet-Kalman filter multiscale estimation frame, we can easily fuse information from multiple sensors sequentially without designing other complex fusion algorithm. The multiscale sequential fusion method can fuse non- 2n sampled data just by analyzing the possible observation structure to d
esign the observation model of the stacked dynamic system. Simulation result of three sensors with sampling interval 1, 2 and 3 shows the efficiency of this method.
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