No abstract

No abstract

This paper propose a fast, matched-filtered based imaging algorithm to detect below ground object To image below ground objects, a set of distributed transmitters and receivers are placed above the ground, or slightly buried. These transmitters radiate waveforms into the subsurface. The resulting wavefront impinges upon underground objects, scattering electromagnetic energy in all directions. Receivers collects the reflected electromagnetic signal, retrieve the phasor of the scattered signals, and transmit this information to systems for post-processing. After applying adaptive signal processing algorithms to collected data, an image of the buried objects can be reconstructed. Reconstructed 2D of buried objects are computed via numerical discretization and match filtering techniques. Match filtering technique is faster and it reduces computational power that required to process the collected data. The matched-filtered approach is easier to implement as compared to matrix inversion. Results from simulation analysis are used to validate this method.

Radio Frequency (RF) Tomography is a mathematical process of 3D image reconstruction from a measurement using a multistatic distribution of transmitters and receivers. The geometric diversity of these elements increases the information in the measurements. The process of determining the permittivity and conductivity profile in the measurement domain, and, therefore, the shape of the target, from the scattered field measurements, is an inverse problem. To solve this problem, under conventional methods such as the Born approximation, we use the principles of linear scattering to determine a linear relationship between measured returns and target shape. The Born approximation is valid if the scatterer is small and does not interact strongly with other objects. However, strong scatterers within the domain may generate sidelobes masking weaker returns. This masking, in conjunction with multipath effects, may result in loss of features and subsequent failure to identify a target. In this research, a novel method is proposed to increase overall image quality and extend the capabilities of RF tomography by modeling the strong scatterers in the measurement domain as dipoles that behave as secondary sources (transmitters). Unlike conventional methods, the dipole model reduces the effects of the sidelobes from the strong scatterers ix