Masters Theses

Keywords and Phrases

IED precursor detection

Abstract

"During the 2003-present Iraq war, Improvised Explosive Devices (IEDs) are being used extensively by the terrorists against the coalition forces and these IEDs were responsible for 40% of coalition force casualties, by the end of 2007. As these IEDs are not based on standard production formulae, their tracking and detection becomes extremely complicated. Laser Induced Breakdown Spectroscopy (LIBS) and Raman Spectroscopy are among the many techniques that have shown promise in detection of explosive compounds. However, the performance of these systems is dependent on the concentration of explosives and ambient noise.

The research presented in this thesis applies signal processing techniques to Raman spectra of a sample to detect the presence of explosives in trace quantities, at a standoff distance. Partial least squares-Discriminant analysis (PLS-DA) was used to identify peaks in the Raman spectra of the sample, which could better differentiate explosive and non-explosive samples. Since peak strengths are vulnerable to noise, our algorithm uses peak energies instead, by fitting Lorentzian or Gaussian curves about the peak locations. An automatic peak detection and fitting algorithm was developed for this purpose. Also, a wavelet based signal denoising algorithm was implemented to remove noise from the Raman Spectra. Further, a multi-sensor fusion algorithm was developed to combine the results from Laser Induced Breakdown Spectroscopy (LIBS) and Raman Spectroscopy to generate more accurate detection results.

The multi-sensor fusion algorithm gave more accurate detection results, a higher probability of detection and lower probability of false alarms, as compared to the results obtained from individual spectroscopic techniques, i.e. Raman Spectroscopy and Laser Induced Breakdown Spectroscopy alone"--Abstract, page iii.

Advisor(s)

Agarwal, Sanjeev, 1971-

Committee Member(s)

Sedigh, Sahra
Moss, Randy Hays, 1953-

Department(s)

Electrical and Computer Engineering

Degree Name

M.S. in Electrical Engineering

Sponsor(s)

United States. Department of the Army

Publisher

Missouri University of Science and Technology

Publication Date

Fall 2010

Pagination

viii, 40 pages

Note about bibliography

Includes bibliographical references.

Rights

© 2010 Abhijeet Singh, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Subject Headings

Improvised explosive devices -- DetectionLaser spectroscopyRaman spectroscopy

Thesis Number

T 9757

Print OCLC #

723179250

Electronic OCLC #

660020563

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