A New Multi-Scale Estimation Scheme for Dynamic System

Author

Funa, Zhou ; Tianhao, Tang ; Chenglin, Wen

Author_Institution

Shanghai Maritime Univ., Shanghai

fYear

2007

Firstpage

396

Lastpage

399

Abstract

Hybrid wavelet-Kalman filter method is an efficient multi-scale estimation method for dynamic system. 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^q, multi-scale estimation problem with non-2ⁿ sampled observation data still hasn´t been efficiently solved, which is an obstacle of multi-scale data fusion. Based on present multi-scale method, we develop a new multi-scale estimation scheme aiming to design the stacked observation model for non-2ⁿ sampled observation by analyzing the possible observation structure of non-2ⁿ sampled sensor.

Keywords

Kalman filters; estimation theory; sampling methods; sensor fusion; wavelet transforms; dynamic system; hybrid wavelet-Kalman filter method; multiscale data fusion; multiscale estimation scheme; sampled observation; stacked observation model; wavelet transform; Control systems; Filters; Gaussian noise; Logistics; Recursive estimation; Sampling methods; Sensor fusion; Sensor systems; Wavelet transforms; White noise; Hybrid wavelet-Kalman filter; Multi-scale data fusion; Non-2ⁿ sample;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference, 2007. CCC 2007. Chinese

Conference_Location

Hunan

Print_ISBN

978-7-81124-055-9

Electronic_ISBN

978-7-900719-22-5

Type

conf

DOI

10.1109/CHICC.2006.4347275

Filename

4347275