Object detection using high resolution near-field array processing

In this paper, we present an algorithm for the detection and localization o f an unknown number of objects buried in a halfspace and present in the nea r held of a linear receiver array. To overcome the nonplanar nature of the wavefield over the array, the full array is divided into a collection of su barrays such that the scattered fields from objects are locally planar at e ach subarray. Using the multiple signal classification (MUSIC) algorithm, d irections of arrival (DOA) of locally planar waves at each subarray are fou nd. By triangulating these DOA's, a set of crossings, condensed around expe cted object locations, are obtained. To process this spatial crossing patte rn, we develop a statistical model for the distribution of these crossings and employ hypotheses testing techniques to identify a collection of small windows likely to contain targets. Finally, the results of the hypothesis t ests are used to estimate the number and locations of the targets. Using si mulated data, we demonstrate the usefulness and performance of this approac h for typical background electrical properties and signal to noise ratios.