Daren R. Wilcox

Landmine signatures in ground-penetrating radar

The Mine Hunter/Killer system employs a ground penetrating radar (GPR). Twenty antennas sample a 3m swath to measure a 3D depth return from the earth as the vehicle moves forward in a lane. Data has been collected on shallow and deep, metal and low metal landmines. Samples signatures from a metal and plastic cased landmines buried at 6 inches are presented. In each example a hyperbolic signature is observed. Two feature sets that exploit the hyperbolic shape for false alarm reduction are presented. The first uses a pixel clustering technique to isolate the hyperbola in 3D. A vector of size/shape features is extracted and combined with a quadratic polynomial discriminant into a single value. The second feature set utilizes the radon transform. The radon transform sums the tails of the hyperbola allowing the algorithm to differentiate between surface clutter, which tends to be oriented horizontally in depth, and the diagonals of the hyperbola. Performance curves for both the 3D size/shape features and the radon feature are presented.

Inexpensive robot for remote detection of UXO

The Electrical and Computer Engineering Technology (ECET) Honors student developed a prototype for an inexpensive unexploded ordinance (UXO) seeking robot. The system provided functionality including: locating metallic landmines and UXO within a defined area/environment, recording the location of said landmines and UXOs, and storing the data off unit via an IEEE 802.11b/g connection to a Windows or Linux-based laptop computer. Application of the prototype and corresponding research may lend themselves to de-mining the more than 100 landmine/unexploded ordinance affected countries in the world particularly in desert terrain (US Department of State Fact Sheet, 2 July 2003).

Radon-transform-based landmine signatures extracted from ground penetrating radar imagery

Step-frequency ground penetrating radar (SFGPR) is a prominent sensor in current buried land mine and unexploded ordnance (UXO) detection systems. Often GPR data is presented in its raw form and it is left to the signal processor to condition the signal. Discussed are the basics of SFGPR and how to condition the data with a minimum of a priori information. Qualitative comparison is shown between first order simulations and measured SFGPR data. Upon conclusion, detection and classification features based on the Radon transform are presented.