Target detection in an oceanic environment using spatial temporal chaos

Chaos has been shown recently to be a powerful tool for modeling sea clutter, radar echoes from a sea surface. However, electromagnetic wave scattering from a rough surface is basically a spatial temporal phenomenon. The paper extended the chaotic sea clutter model to a spatial temporal chaotic model using a novel spatial temporal neural network predictor called radial basis function coupled map lattice (RBF-CML). The RBF-CML predictor was used to reconstruct the spatial temporal dynamic of sea clutter, and was applied to detect small surface targets embedded in sea clutter. Real life radar data were collected to validate the effectiveness of this spatial temporal chaotic approach. Results showed that the spatial temporal approach outperformed the purely temporal method in terms of both sea clutter modeling and target detection