A Ground Probing Radio Detection And Ranging (RADAR) system is used to extract subsurface scans. The principles and practices employed in this research relate to a Ground Probing Radar system operating with high frequency (lGHz) electromagnetic waves with adjacent transmitter and receiver antennas. Based on the theory of Kalman Filters, multi-sensor fusion is used to establish probabilistic models of individual sensor estimates and embed theses descriptions in a team-theoretic framework to finally describe the interactions between different sensors. Applied iteratively, consistent sensor estimates will converge to a qualitative image reconstruction. We investigate the possibility of deriving a functional model of image processing and representation to enhance the visual perception of subsurface images. The research is directed towards qualitative image integration and synthesis based on artificial neural networks pattern classification methods and front-end multi-sensor fusion techniques.


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