Title
Autoregressive Modeling of Raman Spectra for Detection and Classification of Surface Chemicals
Abstract
This paper considers the problem of detecting and classifying surface chemicals by analyzing the received Raman spectrum of scattered laser pulses received from a moving vehicle. An autoregressive (AR) model is proposed to model the spectrum and a two-stage (detection followed by classification) scheme is used to control the false alarm rate. The detector decides whether the received spectrum is from pure background only or background plus some chemicals. The classification is made among a library of possible chemicals. The problem of mixtures of chemicals is also addressed. Simulation results using field background data have shown excellent performance of the proposed approach when the signal-to-noise ratio (SNR) is at least -10 dB.
Year
DOI
Venue
2012
10.1109/TAES.2012.6129647
IEEE Trans. Aerospace and Electronic Systems
Keywords
Field
DocType
Chemicals,Raman scattering,Data models,Noise,Correlation,Detectors,Vehicles
Autoregressive model,Object detection,Data modeling,Electronic engineering,Raman scattering,Laser,Constant false alarm rate,Raman spectroscopy,Detector,Mathematics
Journal
Volume
Issue
ISSN
48
1
0018-9251
Citations 
PageRank 
References 
1
0.38
1
Authors
4
Name
Order
Citations
PageRank
Quan Ding1263.26
S. Kay230940.73
Cuichun Xu3121.67
Darren Emge4364.27