NDLI: Unscented Kalman filtering in the additive noise case

Content Provider	Springer Nature Link
Author	Liu, Ye Yu, AnXi Zhu, JuBo Liang, Dianng
Copyright Year	2010
Abstract	The unscented Kalman filter (UKF) has four implementations in the additive noise case, according to whether the state is augmented with noise vectors and whether a new set of sigma points is redrawn from the predicted state (which is so-called resampling) for the observation prediction. This paper concerns the differences of performances for those implementations, such as accuracy, adaptability, computational complexity, etc. The conditionally equivalent relationships between the augmented and non-augmented unscented transforms (UTs) are proved for several sampling strategies that are commonly used. Then, we find that the augmented and non-augmented UKFs have the same filter results with the additive measurement noise, but only have the same state predictions with the additive process noise. Resampling is not believed to be necessary in some researches. However, we find out that resampling can be helpful for an adaptive Kalman gain. This will improve the convergence and accuracy of the filter when the large scale state modeling bias or unknown maneuvers occur. Finally, some universal designing principles for a practical UKF are given as follows: 1) for the additive observation noise case, it’s better to use the non-augmented UKF; 2) for the additive process noise case, when the small state modeling bias or maneuvers are involved, the non-resampling algorithms with state whether augmented or not are candidates for filters; 3) the resampling and non-augmented algorithm is the only choice while the large state modeling bias or maneuvers are latent.
Starting Page	929
Ending Page	941
Page Count	13
File Format	PDF
ISSN	16747321
Journal	Science in China Series E: Technological Sciences
Volume Number	53
Issue Number	4
e-ISSN	1862281X
Language	English
Publisher	SP Science China Press
Publisher Date	2010-03-20
Publisher Place	Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	unscented Kalman filter additive noise unscented transformation augmented redraw sigma points adaptive Engineering
Content Type	Text
Resource Type	Article
Subject	Engineering Materials Science

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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