Thesis Committee

Fridon Shubitidze, Ph.D. (Chair)

Benjamin Barrowes, Ph.D.

Charles Sullivan, Ph.D.

Abstract

Unexploded ordnance (UXO) is a worldwide problem, which causes deaths and injuries to people living in post-conflict areas. It also forbids former military training sites to be returned to civilian use before they are properly cleaned. UXO cleaning is both dangerous and expensive. Traditional metal detectors can find not only UXO but also non-hazardous metallic clutter as well. Since these technologies were not able to distinguish between targets of interest (TOI) and metallic clutter, UXO clean up could end up with about 95 % of the digging to be metal clutter. To overcome this problem recently new generation electromagnetic induction systems have been developed for subsurface targets detection and classification. One of such system is the Time-Domain Electromagnetic Multisensor Towed Array Detection System (TEMTADS), which provides high fidelity EM data for targets classification. UXO classification consist three main steps: 1: Data collection; 2: Data inversion and targets parameter extraction; 3: Targets discrimination.

In this thesis we present inversion and processing approaches for cued and dynamic TEMTADS data sets. Instead of using the traditional methods of solving inverse problems, we employ the multiple signal classification (MUSIC) algorithm for fast and accurate estimation of targets locations. The MUSIC algorithm is based on the orthogonality between the signal and noise subspaces in the multi static response (MSR) matrix of targets. In general, to identify the boundary between the two subspaces for actual data is a difficult task. To overcome this, the joint-diagonalization (JD) is integrated into the processing. Namely, we use the JD to estimate the number of sources presenting in a data set and to improve signal-to-noise ratio (SNR). The entire process is automated. Studies are done for test stand and blind data sets. Our results show that the combined MUSIC-JD algorithm can be used to estimate targets locations in near real time. 

The JD algorithm is extended further for dynamic TEMTADS data processing and target picking. The algorithm was tested on the data sets collected at live UXO sites in Camp Hale, Colorado.  The studies show that the JD method provides underground target picking and preliminary classification capabilities, given that clear background data can be provided.