Object detection using high resolution near-field array processing

In this paper, we present an algorithm for the detection and localization of an unknown number of objects buried in a halfspace and present in the near 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 subarrays such that the scattered fields from objects are locally planar at each subarray. Using the multiple signal classification (MUSIC) algorithm, directions of arrival (DOA) of locally planar waves at each subarray are found. By triangulating these DOA's, a set of crossings, condensed around expected object locations, are obtained. To process this spatial crossing pattern, 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 tests are used to estimate the number and locations of the targets. Using simulated data, we demonstrate the usefulness and performance of this approach for typical background electrical properties and signal to noise ratios.