Please use this identifier to cite or link to this item:
Title: A new method for generating sigma points and weights for nonlinear filtering
Authors: Date, P
Radhakrishnan, R
Yadav, A
Bhaumik, S
Issue Date: 2018
Publisher: Institiute of Electrical and electronics engineering
Citation: IEEE Control Systems Letters
Abstract: In this paper, a new method termed as new sigma point Kalman filter (NSKF), is proposed for generating sigma points and weights for estimating the states of a stochastic nonlinear dynamic system. The sigma points and their corresponding weights are generated such that the points nearer to the mean (in inner product sense) have a higher probability of occurrence, and the mean vector and covariance matrix are matched exactly. Performance of the new algorithm is compared with the existing unscented Kalman filter (UKF), the cubature Kalman filter (CKF), the cubature quadrature Kalman filter (CQKF) and higher order unscented filter (HOUF) for two different problems. Comparison is done by calculating the root mean square error (RMSE), relative computational time and track-loss. From simulation results, it can be concluded that the proposed algorithm performs with superior estimation accuracy when compared to the UKF, CKF, CQKF and HOUF.
Appears in Collections:Dept of Mathematics Embargoed Research Papers

Files in This Item:
File Description SizeFormat 
Fulltext.pdfEmbargoed until 01 Jun 2020284.09 kBAdobe PDFView/Open    Request a copy

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.