Comparison of optimization-algorithm based feature extraction from time data or time-frequency data for target recognition purposes

Abstract

Ultra-wideband ground penetrating radar (GPR) systems have proved useful for extracting and displaying information for target recognition purposes. Target signatures whether in the time, frequency, or joint time-frequency domains, will substantially depend on the target's burial conditions such as the type of soil, burial depth, and the soil's moisture content. That dependence can be utilized for target recognition purposes as we have demonstrated previously. The signature template of each target was computed in the time-frequency domain from the returned echo when the target was buried at a known depth in the soil with a known moisture content. Then, for any returned echo the relative difference between the similarly computed target signature and a selected signature template was computed. A global optimization method together with our (approximate) target translation method (TTM) that signature difference, chosen as object function, was minimized by adjusting the depth and moisture content, now taken to be unknown parameters. The template that gave the smallest value of the minimized object function for the returned echo was taken as target classification and the corresponding values of the depth and moisture parameters as estimates of the target's burial conditions. This optimization technique can also be applied to time-series data, avoiding the need for time-frequency analysis. It is then of interest to evaluate the relative merits of time data and time-frequency data for target recognition. Such a comparison is here preformed using signals returned from dummy mines buried underground. The results of the analysis serve to assess the intrinsic worth of data in the time domain and in the time-frequency domain for identifying subsurface targets using a GPR. The targets are buried in a test field at the Swedish Explosive Ordnance Disposal and Demining Center (SWEDEC) at Eksjo, Sweden.