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State estimation of nonlinear dynamical systems using nonlinear update based Unscented Gaussian Sum Filter

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Title State estimation of nonlinear dynamical systems using nonlinear update based Unscented Gaussian Sum Filter
 
Creator KOTTAKKI, KK
BHARTIYA, S
BHUSHAN, M
 
Subject Nonlinear state estimation
Unscented Kalman Filter
Sum of Gaussians
KALMAN FILTER
REACTOR
 
Description Two attractive features of Unscented Kalman Filter (UKF) are: (1) use of deterministically chosen points (called sigma points), and (2) only a linear dependence of the number of sigma points on the number of states. However, an implicit assumption in UKF is that the prior conditional state probability density and the state and measurement noise densities are Gaussian. To avoid the restrictive Gaussianity assumption, Gaussian Sum-UKF (GS-UKF) has been proposed in literature that approximates all the underlying densities using a sum of Gaussians. However, the number of sigma points required in this approach is significantly higher than in UKF, thereby making GS-UKF computationally intensive. In this work, we propose an alternate approach, labeled Unscented Gaussian Sum Filter (UGSF), for state estimation of nonlinear dynamical systems, corrupted by Gaussian state and measurement noises. Our approach uses a Sum of Gaussians to approximate the non-Gaussian prior density. A key feature of this approximation is that it is based on the same number of sigma points as used in UKF, thereby resulting in similar computational complexity as UKF. We implement the proposed approach on two nonlinear state estimation case studies and demonstrate its utility by comparing its performance with UKF and GS-UKF. (C) 2014 Elsevier Ltd. All rights reserved.
 
Publisher ELSEVIER SCI LTD
 
Date 2014-12-28T16:40:51Z
2014-12-28T16:40:51Z
2014
 
Type Article
 
Identifier JOURNAL OF PROCESS CONTROL, 24(9)1425-1443
0959-1524
1873-2771
http://dx.doi.org/10.1016/j.jprocont.2014.06.013
http://dspace.library.iitb.ac.in/jspui/handle/100/16909
 
Language English