Title :
An Innovation Filtering Multiple Model Algorithm for Integrated Navigation System
Author :
Rong-chun, Zang ; Ping-yuan, Cui
Author_Institution :
Harbin Inst. of Technol.
fDate :
Aug. 30 2006-Sept. 1 2006
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;
Conference_Titel :
Innovative Computing, Information and Control, 2006. ICICIC '06. First International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7695-2616-0
DOI :
10.1109/ICICIC.2006.417