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

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...

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Bibliographic Details
Published in:Frontiers of information technology & electronic engineering 2011-08, Vol.12 (8), p.678-686
Main Authors: Ge, Quan-bo, Li, Wen-bin, Wen, Cheng-lin
Format: Article
Language:English
Subjects:
Algorithms
Bearing (direction)
Communications Engineering
Computer Hardware
Computer Science
Computer Systems Organization and Communication Networks
Correlation
Electrical Engineering
Electronics and Microelectronics
Instrumentation
Kalman filters
Maneuvers
Networks
Nonlinear filters
State estimation
Tracking filters
Tracking systems
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Summary: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 eovariance 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.
ISSN:1869-1951
2095-9184
1869-196X
2095-9230
DOI:10.1631/jzus.C10a0353