Autoregressive Modeling of Raman Spectra for Detection and Classification of Surface Chemicals


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.

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