NDLI: The linear fitting Kalman filter for nonlinear tracking

Content Provider	IEEE Xplore Digital Library
Author	Yuanbo Xiong Xionghu Zhong Rong Yang
Copyright Year	2015
Description	Author affiliation: Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore (Xionghu Zhong) \|\| Coll. of Archit. & Environ., Sichuan Univ., Chengdu, China (Yuanbo Xiong)
Abstract	Dynamic estimation in various synthetic aperture radar (SAR) applications often involves nonlinear models. The first-order Taylor approximation is usually used to linearize the nonlinear functions to perform estimation efficiently. However, the error generated by the first-order Taylor approximation cannot be negligible when dynamic systems are high nonlinearity or with large input errors. This paper proposes a linearization method by minimizing the difference between the nonlinear function and its linear approximation, and yields a better linear fitting function. A Kalman filter based on this principle is suggested. Simulation tests are conducted among the proposed linear fitting Kalman filter (LFKF), the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). The results show that the LFKF obviously outperforms the EKF which uses the first-order Taylor approximation, and the LFKF can achieve similar estimation accuracy of the UKF with less computational cost.
Starting Page	511
Ending Page	515
File Size	148834
Page Count	5
File Format	PDF
e-ISBN	9781467372978
DOI	10.1109/APSAR.2015.7306261
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-01
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Linear approximation Estimation Approximation algorithms Computational efficiency Kalman filters
Content Type	Text
Resource Type	Article

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