Title :
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
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;
Conference_Titel :
Signal Processing (ICSP), 2014 12th International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4799-2188-1
DOI :
10.1109/ICOSP.2014.7014972
