DocumentCode
2199956
Title
Non linear optimum filter based smoothing Interacting Multiple Model for GPS navigation system
Author
Malleswaran, M. ; Vaidehi, V. ; Ramesh, H. ; Bruntha, P. Malin
Author_Institution
Dept. of ECE, Anna Univ. of Technol, Tirunelveli (AUTT), Tirunelveli, India
fYear
2012
fDate
19-21 April 2012
Firstpage
383
Lastpage
388
Abstract
An Interacting Multiple Model Unscented Two Filter Smoother (IMM-UTFS) approach for GPS navigation system is introduced in this paper. The Unscented Kalman Filter (UKF) propagates its state estimate and covariance through unscented transform without any need of linearization. The Interacting Multiple Model (IMM) algorithm obtains its estimate by combining the individual estimate from a number of parallel filters matched to different motion models of the vehicle. This paper adopts the Unscented Two Filter Smoother to the IMM algorithm to increase the navigation estimation accuracy. The dynamic behavior of the vehicle is analyzed and the simulation results show that IMM-UTFS can improve overall navigation accuracy as compared to traditional filters like UKF and multiple model filters like IMM-UKF.
Keywords
Global Positioning System; Kalman filters; nonlinear filters; vehicle dynamics; GPS navigation system; Global Positioning System; IMM-UTFS approach; navigation estimation accuracy; nonlinear optimum filter; parallel filter; smoothing interacting multiple model; unscented Kalman filter; unscented two filter smoother approach; vehicle dynamic behavior; Estimation; Filtering algorithms; Navigation; Noise; Smoothing methods; Vehicle dynamics; Vehicles; GPS; Interacting Multiple Model (IMM); Interacting Multiple Model Unscented Two Filter Smoother (IMM-UTFS); Unscented Kalman Filter (UKF);
fLanguage
English
Publisher
ieee
Conference_Titel
Recent Trends In Information Technology (ICRTIT), 2012 International Conference on
Conference_Location
Chennai, Tamil Nadu
Print_ISBN
978-1-4673-1599-9
Type
conf
DOI
10.1109/ICRTIT.2012.6206794
Filename
6206794
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