NDLI: A new adaptive maneuvering target tracking algorithm using artificial neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Zhijun Yu Jianming Wei Haitao Liu
Copyright Year	2008
Description	Author affiliation: Shanghai Inst. of Microsyst. & Inf. Technol., Chinese Acad. of Sci., Shanghai (Zhijun Yu; Jianming Wei; Haitao Liu)
Abstract	A new neural network (NN) aided adaptive unscented Kalman filter (UKF) is presented for tracking high maneuvering target. In practice, the dynamic systems of many target tracking problems are usually nonlinear and incompletely observed, moreover, there may be large modeling errors when the target is maneuverable or some parameters of the system models are inaccurate or incorrect. The adaptive capability of filters is known to be increased by incorporating a neural network into the filtering procedure. On the other hand, some nonlinear filtering methods such as extended Kalman Filter (EKF) have been used to train a NN with fast convergence speed by augmenting the state with unknown connecting weights. Tackling the natural coalescent between the filtering algorithm and the NN described above, first a more efficient learning algorithm based on unscented Kalman filter (UKF) is derived, which can give a more accurate estimate of the weights and possess faster convergence rate. We then extend the algorithm to form a new NN aided adaptive UKF algorithm and use it in maneuvering target tracking applications. The NN in this algorithm is used to approximate the uncertainty of system models and is trained online, together with the target state estimation. Some simulations are also given to validate that the proposed method can give well state estimation of a highly maneuvering target.
Starting Page	901
Ending Page	905
File Size	315016
Page Count	5
File Format	PDF
ISBN	9781424418206
ISSN	10987576
DOI	10.1109/IJCNN.2008.4633905
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Joints
Content Type	Text
Resource Type	Article

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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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