Agent identification using a sparse Bayesian model

Huiping Duan, Hongbin Li, Jing Xie, Nicolai S. Panikov, Hong Liang Cui

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Identifying agents in a linear mixture is a fundamental problem in spectral sensing applications including chemical and biological agent identification. In general, the size of the spectral signature library is usually much larger than the number of agents really present. Based on this fact, the sparsity of the mixing coefficient vector can be utilized to help improve the identification performance. In this paper, we propose a new agent identification method by using a sparse Bayesian model. The proposed iterative algorithm takes into account the nonnegativity of the abundance fractions and is proved to be convergent. Numerical studies with a set of ultraviolet (UV) to infrared (IR) spectra are carried out for demonstration. The effect of the signature mismatch is also studied using a group of terahertz (THz) spectra.

Original languageEnglish
Article number5735153
Pages (from-to)2556-2564
Number of pages9
JournalIEEE Sensors Journal
Volume11
Issue number10
DOIs
StatePublished - 2011

Keywords

  • Agent identification
  • false alarm
  • linear mixture
  • mismatch
  • signature
  • sparse Bayesian model
  • spectral sensing

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