Resolving Wall Ambiguities Using Angular Diverse Synthetic Arrays

Model-based algorithms that attempt to localize targets and estimate the structure within a building using data from external sensors have received much attention in recent years. The potential benefits to homeland security and urban warfare are exceedingly apparent. Accurately estimating the thickness and dielectric constant of the exterior wall could prove to be a critical first step in determining the layout within, i.e., the location of interior walls, doorways, stairwells, etc. However, data collection using a linear sensor arrangement yields an ambiguous two dimensional objective function for the wall parameters, rendering maximum likelihood methods ineffective. We show that a spatially diverse aperture obviates the wall parameter ambiguity and allows accurate estimation of thickness and permittivity using dynamic logic, an iterative model-based approach to maximum likelihood.