Computational electromagnetic modeling & simulation of ultra wideband sub-surface sensors for the detection and imaging of buried objects using spatial and spectral diversity

An enhanced remote sensing technique for the detection and identification of deeply buried objects is presented in this paper. A new RF Tomographic Technique is proposed for developing RF CAT Scans of buried objects using spectral and spatial diversity. This imaging technique uses an embedded ring of subsurface radiators as the source of strong underground radiated transmissions. Distributed surface-contact sensors are used to collect the tomographic data for relay to a remote control site. Three-dimensional numerical imaging algorithms have been developed to detect, image, and characterize deeply buried objects. Distributed transmitters and receivers significantly increase unwanted mutual coupling and EM emissions that interfere with signal reception; however, by embedding the transmitters underground, reduced mutual coupling and EM emissions, and improved signal-to-noise ratios, can be achieved. Simple 2D surface SAR experiments over deep mine shafts were performed to validate and verify (V&V) the 3D processing algorithms using 2D surface SAR sensor data. The WIPL-D CEM Code was used to model and simulate (M&S) the embedded and distributed sensors and to verify the significant enhancement in the received signal-to-noise ratio obtained by burying the radiating antennas.