Data Fusion Estimation of Inertial Sensors Based on Multiscale Stochastic Dynamic Models

In the paper, combing with discrete wavelet transform, dynamic system theory and stochastic process theory establish multiscale stochastic dynamic models considering scale as variable and present multiscale fusion estimation algorithm in order to realize the optimum estimation of the state. The algo...

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Bibliographic Details
Published in2007 International Conference on Mechatronics and Automation pp. 647 - 651
Main Authors Xuemei Zhou, Jiantong Wu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2007
Subjects
Aerodynamics
Discrete wavelet transforms
gyro calibration
Kalman filters
Mathematics
multiscale fusion estimation
Sensor fusion
Sensor phenomena and characterization
Sensor systems
State estimation
stochastic dynamic model
Stochastic processes
Stochastic systems
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Summary:In the paper, combing with discrete wavelet transform, dynamic system theory and stochastic process theory establish multiscale stochastic dynamic models considering scale as variable and present multiscale fusion estimation algorithm in order to realize the optimum estimation of the state. The algorithm may be a method used in no state model. Using the algorithm for gyro signals processing and fusing the observation at different scales, the accuracy is improved obviously. Simulation and test all prove that the algorithm is available.
ISBN:9781424408276
142440827X
ISSN:2152-7431
2152-744X
DOI:10.1109/ICMA.2007.4303619