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Nonlinear spatial-temporal prediction based on optimal fusion

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2 Author(s)
Youshen Xia ; Dept. of Electr. & Comput. Eng, Calgary Univ., Alta., Canada ; Henry Leung

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.

Published in:

Neural Networks, IEEE Transactions on  (Volume:17 ,  Issue: 4 )