Design of adaptive robust square-root cubature Kalman filter with noise statistic estimator

Liqiang Zhao, Jianlin Wang, Tao Yu, Huan Jian and Tangjiang Liu

Applied Mathematics and Computation, 2015, vol. 256, issue C, 352-367

Abstract: To solve the problem that when the model uncertainties exists or the prior noise statistics are unknown, the accuracy of the cubature Kalman filter (CKF) will decline or diverge, an adaptive robust square-root CKF (ARSCKF) algorithm with the noise statistic estimator is proposed. Firstly, the square-root version of the CKF (SCKF) algorithm is given when the means of process and measurement Gaussian noise sequences are nonzero. Secondly, based on strong tracking filter principle, the robust SCKF is designed to accommodate modeling uncertainties. Thirdly, the suboptimal unbiased constant noise statistic estimator based on the principle of maximum a posterior (MAP) is derived; on this basis, the recursive formula of the time variant noise statistic estimator using exponential weighted method is provided and then the implementation process of the ARSCKF algorithm is constructed. Finally, the efficacy of the ARSCKF is demonstrated by the numerical experiments of a high-dimensional target tracking system.

Keywords: Cubature Kalman filter; Strong tracking filter; Noise statistic estimator; MAP (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0096300314016907
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:256:y:2015:i:c:p:352-367

DOI: 10.1016/j.amc.2014.12.036

Access Statistics for this article

Applied Mathematics and Computation is currently edited by Theodore Simos

More articles in Applied Mathematics and Computation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().