Abstract: An optomechanical switching device, a control system, and a graphical user interface for a photopolarimetric lidar standoff detection that employs differential-absorption Mueller matrix spectroscopy. An output train of alternate continuous-wave CO2 laser beams [ . . . L1:L2 . . . ] is directed onto a suspect chemical-biological (CB) aerosol plume or the land mass it contaminates (S) vis-à-vis the OSD, with L1 [L2] tuned on [detuned off] a resonant molecular absorption moiety of CB analyte. Both incident beams and their backscattered radiances from S are polarization-modulated synchronously so as to produce gated temporal voltage waveforms (scattergrams) recorded on a focus at the receiver end of a sensor (lidar) system. All 16 elements of the Mueller matrix (Mij) of S are measured via digital or analog filtration of constituent frequency components in these running scattergram data streams (phase-sensitive detection).

Abstract: Chemical agent warfare materials and their simulant liquids are identified on terrestrial surfaces at a distance by recognizing the contaminant's infrared fingerprint spectrum brought out in thermal luminescence (TL). Suspect surfaces are irradiated with microwave light that is absorbed into the surface and, subsequently, TL is released by the surface. An optics receiver collects the released TL radiant light, and a data acquisition system searches this TL radiant flux for the contaminant's fingerprint infrared spectrum. A decision on the presence or absence of any-of-N contaminants is done by a neural network system that acts as a filter through real-time pattern recognition of the contaminant's unique infrared absorption or emission spectra.

Abstract: A four-layer neural network is trained with data of midinfrared absorption by nerve and blister agent compounds (and simulants of this chemical group) in a standoff detection application. Known infrared absorption spectra by these analyte compounds and their computed first derivative are scaled and then transformed into binary or decimal arrays for network training by a backward-error-propagation (BEP) algorithm with gradient descent paradigm. The neural network transfer function gain and learning rate are adjusted on occasion per training session so that a global minimum in final epoch convergence is attained. Three successful neural network filters have been built around an architecture design containing: (1) an input layer of 350 neurons, one neuron per absorption intensity spanning 700.ltoreq..nu..ltoreq.1400 wavenumbers with resolution .DELTA..nu.