Statistical method to detect subsurface objects using array ground-penetrating radar data

Abstract

Abstract-We introduce a combination of high-dimensional analysis of variance (HANOVA) and sequential probability ratio test (SPRT) to detect buried objects from an array ground-penetrating radar (GPR) surveying a region of interest in a progressive manner. Using HANOVA, we exploit the transient characteristic of GPR signals in the time domain to extract information about buried objects at fixed positions of the array. Based on the output of the HANOVA, the SPRT is employed to make detection decisions recursively as the array moves downtrack. The method is on-line implementable and of low computational complexity. Our approach is validated using field-data from two quite different GPR sensing systems designed for landmine detection applications.

Critical Analysis of Background Subtraction Techniques on Real GPR Data

“…These approaches generally require the parallel data slices of GPR to form an image and are thus not well suited to real time computations in which data is processed sequentially. A third method is statistical signal processing method 18 , where some statistical hierarchy develops to remove the background using either probability of detection, variance, Eigen values or probability of false-alarm 16 . Selection criterion of background subtraction techniques are another major issue in GPR applications because of the following reasons:…”

“…The GPR technology provides the detection capability and identification capability of buried objects and it is best suitable technology for interpreting the target and the ground informations 1,2 . As compared to existing techniques, the GPR can speedily and non-incursively create high resolution image of underground buried objects [3][4][5][6][7][8] . In GPR, a challenging problem is to discriminate the buried target backscatter field from the rest of the backscatter field, which is outside the area of investigation.…”

Bayesian compressive sensing for ultrawideband inverse scattering in random media

“…Target parameters may include location, size, orientation, and material properties [1][2][3][4][5][6]. Electromagnetic inverse scattering finds many applications in medical imaging [7][8][9][10][11][12], through-wall imaging [13][14][15], non-destructive testing [16][17][18], ground-penetrating radar [19][20][21][22], and geophysical exploration in general [3,4,23,24].…”