A heuristic for sigma set selection of UKF

Author

Yujin Wang ; Jiang Liu ; Wenqiang Yang ; Ju Zhang

Author_Institution

Coll. of Comput. Sci. & Technol., Chongqing Univ. of Posts & Telecommun., Chongqing, China

fYear

2014

fDate

19-23 Oct. 2014

Firstpage

72

Lastpage

77

Abstract

In this paper we present a higher order moment-matching algorithm for computing the distribution parameters of nonlinear transformation random variables. The new algorithm has two distinct aspects compared to the standard Unscented Kalman Filter (UKF). First, the sigma points are computed in two steps using the covariance matrix and higher-order moments. Second, the associated weights are positive numbers in the interval [0, 1]. The performance of the new algorithm is illustrated by simulation. Results show improvement in accuracy in comparison to the traditional UKF.

Keywords

Kalman filters; covariance matrices; method of moments; nonlinear filters; UKF; associated weights; covariance matrix; distribution parameter computation; higher order moment-matching algorithm; nonlinear transformation random variables; positive numbers; sigma set point selection; unscented Kalman filter; Accuracy; Approximation methods; Computational modeling; Covariance matrices; Equations; Kalman filters; Mathematical model; State estimation; high order moment matching; sigma set; unscented Kalman filter;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing (ICSP), 2014 12th International Conference on

Conference_Location

Hangzhou

ISSN

2164-5221

Print_ISBN

978-1-4799-2188-1

Type

conf

DOI

10.1109/ICOSP.2014.7014972

Filename

7014972