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Comparison of Unscented and Extended Kalman Filters with Application in Vehicle Navigation

Published online by Cambridge University Press:  29 September 2016

Cheng Yang ,
Wenzhong Shi  and
Wu Chen
Cheng Yang*
Affiliation:
(The Hong Kong Polytechnic University, Hong Kong, China)
Wenzhong Shi
Affiliation:
(The Hong Kong Polytechnic University, Hong Kong, China)
Wu Chen
Affiliation:
(The Hong Kong Polytechnic University, Hong Kong, China)
*
(E-mail: c.yang@connect.polyu.hk)
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Abstract

The Unscented Kalman Filter (UKF) is a well-known nonlinear state estimation method. It shows superior performance at nonlinear estimation compared to the Extended Kalman Filter (EKF). This paper is devoted to an investigation between UKF and EKF with different feedback control modes in vehicle navigation. Theoretical formulation, simulation and field tests have been carried out to compare the performance of UKF and EKF. The simulation and test results demonstrate that the estimated state of a UKF relies on the measurements and is less sensitive to historical model information. The results also indicate that UKF has benefits for prototype model design due to avoidance of calculation of a Jacobian matrix. EKF, however, is more computationally efficient and more stable.

Keywords

Kalman FilteringNonlinear FilteringUnscented Kalman FilterLand vehicle Navigation
Type
Research Article
Information
The Journal of Navigation , Volume 70 , Issue 2 , March 2017 , pp. 411 - 431
Copyright
Copyright © The Royal Institute of Navigation 2016 

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