DocumentCode
2526421
Title
An Innovation Filtering Multiple Model Algorithm for Integrated Navigation System
Author
Rong-chun, Zang ; Ping-yuan, Cui
Author_Institution
Harbin Inst. of Technol.
Volume
3
fYear
2006
fDate
Aug. 30 2006-Sept. 1 2006
Firstpage
394
Lastpage
397
Abstract
An interacting multiple model unscented Kalman filter (IMM-UKF) was proposed for the two problems of the nonlinear filtering i.e. nonlinearity and noise. The uncertainty of the noise can be described by a set of switching models. This modeling approach makes it possible to employ the multiple model estimation (MME) combining with UKF to deal with the problem of nonlinear filtering with uncertainty noise. An innovation filtering is introduced in the MME, which can decrease the measurement noise and derive more accurate statistic information for weight calculation. The application of the algorithm on GPS/DR integrated navigation system demonstrated that the method was feasible and accurate
Keywords
Global Positioning System; Kalman filters; estimation theory; noise; nonlinear filters; GPS-DR integrated navigation system; innovation filtering multiple model algorithm; interacting multiple model unscented Kalman filter; measurement noise; multiple model estimation; nonlinear filtering; switching models; uncertainty noise; Filtering algorithms; Gaussian distribution; Information filtering; Information filters; Jacobian matrices; Navigation; Noise measurement; Nonlinear filters; Sampling methods; Technological innovation;
fLanguage
English
Publisher
ieee
Conference_Titel
Innovative Computing, Information and Control, 2006. ICICIC '06. First International Conference on
Conference_Location
Beijing
Print_ISBN
0-7695-2616-0
Type
conf
DOI
10.1109/ICICIC.2006.417
Filename
1692197
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