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| Title: | Sensor data fusion for spectroscopy-based detection of explosives |
| Author (s): | Shah, Pratik V. Singh, Abhijeet Agarwal, Sanjeev Sedighsarvestani, Sahra Ford, Alan Waterbury, Robert |
| Department/Lab Affiliations: | Center for Infrastructure Engineering Studies Electrical and Computer Engineering Intelligent Systems Center |
| Keywords: | RDX TNT detection explosive compounds residue |
| Subject Terms: | Ammonium nitrate. |
| Issue Date: | 2009-05 |
| Publisher: | Society of Photo-Optical Instrumentation Engineers SPIE |
| Citation: | Shah, Pratik V., Abhijeet Singh, Sanjeev Agarwal, Sahra Sedigh, Alan Ford, and Robert Waterbury. “Sensor data fusion for spectroscopy-based detection of explosives”, Proceedings of SPIE, Vol. 7303, 730329 (2009). |
| Abstract: | 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. |
| Type: | Article - Conference proceedings text |
| In Title: | Proceedings of SPIE |
| Copyright Notice: | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. No full text allowed FULL COPYRIGHT INFORMATION: |
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| title | Sensor data fusion for spectroscopy-based detection of explosives |
| contributor.author | Shah, Pratik V. |
| contributor.author | Singh, Abhijeet |
| contributor.author | Agarwal, Sanjeev |
| contributor.author | Sedighsarvestani, Sahra |
| contributor.author | Ford, Alan |
| contributor.author | Waterbury, Robert |
| contributor.deptlab | Center for Infrastructure Engineering Studies |
| contributor.deptlab | Electrical and Computer Engineering |
| contributor.deptlab | Intelligent Systems Center |
| subject | RDX |
| subject | TNT |
| subject | detection |
| subject | explosive compounds |
| subject | residue |
| subject.LCSH | Ammonium nitrate. |
| date.issued | 2009-05 |
| publisher | Society of Photo-Optical Instrumentation Engineers SPIE |
| identifier.citation | Shah, Pratik V., Abhijeet Singh, Sanjeev Agarwal, Sahra Sedigh, Alan Ford, and Robert Waterbury. “Sensor data fusion for spectroscopy-based detection of explosives”, Proceedings of SPIE, Vol. 7303, 730329 (2009). |
| identifier.pub.URI | |
| description.abstract | 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. |
| type | Article - Conference proceedings |
| type.DCMIType | text |
| relation.isPartOf | Proceedings of SPIE |
| rights | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. |
| rights | No full text allowed |
| rights.URI | |
| identifier.persist.URI | |
| date.available | 2009-06-19T14:43:44Z |