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On-line Access: 2011-08-03

Received: 2010-07-29

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Journal of Zhejiang University SCIENCE C 2011 Vol.12 No.8 P.615-628

http://doi.org/10.1631/jzus.C10a0353


SCKF-STF-CN: a universal nonlinear filter for maneuver target tracking


Author(s):  Quan-bo Ge, Wen-bin Li, Cheng-lin Wen

Affiliation(s):  Institute of Information and Control, Hangzhou Dianzi University, Hangzhou 310018, China, State Key Lab of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   qbge@hdu.edu.cn, wencl@hdu.edu.cn

Key Words:  Nonlinear system, Maneuver target tracking, Correlated noises, Square-root cubature Kalman filter (SCKF), Strong tracking filtering (STF)


Quan-bo Ge, Wen-bin Li, Cheng-lin Wen. SCKF-STF-CN: a universal nonlinear filter for maneuver target tracking[J]. Journal of Zhejiang University Science C, 2011, 12(8): 615-628.

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Abstract: 
square-root cubature Kalman filter (SCKF) is more effective for nonlinear state estimation than an unscented Kalman filter. In this paper, we study the design of nonlinear filters based on SCKF for the system with one step noise correlation and abrupt state change. First, we give the SCKF that deals with the one step correlation between process and measurement noises, SCKF-CN in short. Second, we introduce the idea of a strong tracking filter to construct the adaptive square-root factor of the prediction error covariance with a fading factor, which makes SCKF-CN obtain outstanding tracking performance to the system with target maneuver or abrupt state change. Accordingly, the tracking performance of SCKF is greatly improved. A universal nonlinear estimator is proposed, which can not only deal with the conventional nonlinear filter problem with high dimensionality and correlated noises, but also achieve an excellent strong tracking performance towards the abrupt change of target state. Three simulation examples with a bearings-only tracking system are illustrated to verify the efficiency of the proposed algorithms.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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