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Mathematical Problems in Engineering Volume 2019 ,2019-01-17
Adaptively Random Weighted Cubature Kalman Filter for Nonlinear Systems
Research Article
Zhaohui Gao 1 Dejun Mu 1 Yongmin Zhong 2 Chengfan Gu 3 Chengcai Ren 4
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DOI:10.1155/2019/4160847
Received 2018-09-07, accepted for publication 2018-12-16, Published 2018-12-16
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摘要

This paper presents a new adaptive random weighting cubature Kalman filtering method for nonlinear state estimation. This method adopts the concept of random weighting to address the problem that the cubature Kalman filter (CKF) performance is sensitive to system noise. It establishes random weighting theories to estimate system noise statistics and predicted state and measurement together with their associated covariances. Subsequently, it adaptively adjusts the weights of cubature points based on the random weighting estimations to improve the prediction accuracy, thus restraining the disturbances of system noises on state estimation. Simulations and comparison analysis demonstrate the improved performance of the proposed method for nonlinear state estimation.

授权许可

Copyright © 2019 Zhaohui Gao et al. 2019
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

通讯作者

Zhaohui Gao.School of Automatics, Northwestern Polytechnical University, Xi’an 710072, China, nwpu.edu.cn.alexandergao@mail.nwpu.edu.cn

推荐引用方式

Zhaohui Gao,Dejun Mu,Yongmin Zhong,Chengfan Gu,Chengcai Ren. Adaptively Random Weighted Cubature Kalman Filter for Nonlinear Systems. Mathematical Problems in Engineering ,Vol.2019(2019)

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