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
Link To Document :
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