Abstract

The effective and reliable detection of explosive compounds in complex environments is an important problem in many environment and security-related applications. This paper develops an explosive detection approach based on multi-modal sensing and sensor data fusion. A least-squares feature extraction technique is designed to isolate explosive signatures in data collected using electrochemical and polymer nanojunction sensors. The information obtained from the two sensors is then efficiently combined using a Bayesian decision fusion scheme. Results are presented for the detection of the explosive compound TNT showing the merit of the proposed approach.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
Pages2918-2921
Number of pages4
DOIs
StatePublished - Nov 8 2010
Event2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States
Duration: Mar 14 2010Mar 19 2010

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
CountryUnited States
CityDallas, TX
Period3/14/103/19/10

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Keywords

  • Electrochemical sensing
  • Explosive detection
  • Feature extraction
  • Least-squares
  • Sensor fusion

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Kovvali, N., Prior, C., Cizek, K., Galik, M., Diaz, A., Forzani, E., Cagan, A., Wang, J., Tao, N., Cochran, D., Spanias, A., & Tsui, R. (2010). Least-squares based feature extraction and sensor fusion for explosive detection. In 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings (pp. 2918-2921). [5496159] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2010.5496159