In-situ trace detection of explosive compounds such as RDX, TNT, and ammonium nitrate, is an important
problem for the detection of IEDs and IED precursors. Spectroscopic techniques such as LIBS and Raman have
shown promise for the detection of residues of explosive compounds on surfaces from standoff distances. Individually,
both LIBS and Raman techniques suffer from various limitations, e.g., their robustness and reliability
suffers due to variations in peak strengths and locations. However, the orthogonal nature of the spectral and
compositional information provided by these techniques makes them suitable candidates for the use of sensor
fusion to improve the overall detection performance. In this paper, we utilize peak energies in a region by fitting
Lorentzian or Gaussian peaks around the location of interest. The ratios of peak energies are used for discrimination,
in order to normalize the effect of changes in overall signal strength. Two data fusion techniques are
discussed in this paper. Multi-spot fusion is performed on a set of independent samples from the same region
based on the maximum likelihood formulation. Furthermore, the results from LIBS and Raman sensors are
fused using linear discriminators. Improved detection performance with significantly reduced false alarm rates is
reported using fusion techniques on data collected for sponsor demonstration at Fort Leonard Wood.
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