Nonlinear spatial-temporal prediction based on optimal fusion.
The problem of spatial-temporal signal processing and modeling has been of great interest in recent years. A new spatial-temporal prediction method is presented in this paper. An optimal fusion scheme based on fourth-order statistic is first employed to combine the received signals at different spatial domains. The fused signal is then used to construct a spatial-temporal predictor by a support vector machine. It is shown theoretically that the proposed method has an improved performance even in non-Gaussian environments. To demonstrate the practicality of this spatial-temporal predictor, we apply it to model real-life radar sea scattered signals. Experimental results show that the proposed method can provide a more accurate model for sea clutter than the conventional methods.
Xia
Youshen
Y
Leung
Henry
H
D000465:Algorithms; D005544:Forecasting; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D013997:Time Factors