Data Fusion in Radial Basis Function Networks for Spatial Regression
Neural Processing Letters
Novel Coupled Map Lattice Model for Prediction of EEG Signal
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Comparison between analog and digital neural network implementations for range-finding applications
IEEE Transactions on Neural Networks
Method of radar detecting small signal based on adaptive genetic algorithm and neural network
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
EURASIP Journal on Advances in Signal Processing - Special issue on advances in signal processing for maritime applications
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 2
Nonlinear dynamics of EEG signal based on coupled network lattice model
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
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From observation sea clutter, radar echoes from a sea surface, is chaotic rather than random. We propose the use of a spatial temporal predictor to reconstruct the chaotic dynamic of sea clutter because electromagnetic wave scattering is a spatial temporal phenomenon which is physically modeled by partial differential equations. The spatial temporal predictor used here is called radial basis function coupled map lattice (RBF-CML) which uses linear combination to fuse either measurements in different spatial domains for an RBF prediction or predictions from several RBF nets operated on different spatial regions. Using real-life radar data, it is shown that the RBF-CML is an effective method to reconstruct the sea clutter dynamic. The RBF-CML predictor is then applied to detect small targets in sea clutter using the constant false alarm rate (CFAR) principle. The spatial temporal approach is shown, both theoretically and experimentally, to be superior to a conventional CFAR detector