Infrared dim small target track predicting using least squares support vector machine

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 σ 2 . Since the optimization parameters (γ, σ 2 ) 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.