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Proceedings Paper

Infrared dim small target track predicting using least squares support vector machine
Author(s): Guangping Wang; Kun Gao; Guoqiang Ni
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Paper Abstract

Compared with Support Vector Machine (SVM), Least Squares Support Vector Machine (LS-SVM) has overcome the shortcoming of higher computational burden by solving linear equations, and has been widely used in classification and nonlinear function estimation. For dim small targets track predicting in the IR image sequences, a new method based on LS-SVM is proposed. LS-SVM has prominent advantages in model selecting, over-fitting overcoming and local minimum overcoming. In this paper, the RBF kernel function is used in LS-SVM, so there are two parameters in LS-SVM: the regularization parameter γ and the kernel width parameter σ2. Since the optimization parameters (γ, σ2) determine the performance of LS-SVM, so their influence on the performance of LS-SVM is analyzed in this paper. Finally, compared with the Least Square (LS) estimation, the experiments show that LS-SVM can track targets more precisely and more robustly than LS. Experiments show that the track predicting method based on LS-SVM possesses the strong learning capability through a small quantity of samples, the good characteristic of generalization and rejection to random noise. It is a potential track predicting method.

Paper Details

Date Published: 8 January 2008
PDF: 8 pages
Proc. SPIE 6835, Infrared Materials, Devices, and Applications, 68351Q (8 January 2008); doi: 10.1117/12.756581
Show Author Affiliations
Guangping Wang, Beijing Institute of Technology (China)
Kun Gao, Beijing Institute of Technology (China)
Guoqiang Ni, Beijing Institute of Technology (China)

Published in SPIE Proceedings Vol. 6835:
Infrared Materials, Devices, and Applications
Yi Cai; Haimei Gong; Jean-Pierre Chatard, Editor(s)

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