ABSTRACT
A modeling and simulation study of the limits of remote detection by passive IR has led to a new concept for the remote detection of hazardous clouds. A passive IR signature model was developed with the Edgewood Research, Development, and Engineering Center IR spectral data bases used as input for chemicals and biologicals and with the atmospheric transmittance model used for MODTRAN. The cloud travel and dispersion model, VLSTRACK, was used to simulate chemical and biological clouds. An easily applied spectral discrimination technique was developed with a standard Mathematica version of linear programming. All these were melded with Mathematica to produce images of three threat clouds: Sarin, mustard, and an unnamed biological. The hazardous cloud imager is a spatially scanning Fourier transform IR on the same level of complexity as conventional remote detectors, but is capable of greater sensitivity and moving operation.
ABSTRACT
Passive infrared remote detection of hazardous gases, vapors, and aerosols is based on the difference, Δ T, between the air temperature of the threat vapor cloud and the effective radiative temperature of the background. In this paper I address the problem of detection with a low-angle-sky background. I used Modtran to predict Δ T and atmospheric transmittance for standard atmospheric models. The detection limits, at 2-cm(-1) resolution, are discussed for sulfur hexafluoride, Sarin, trichloroethylene, methyl isocyanate, mustard gas, methyl chloride, and sulfur dioxide for selected cases with the U.S. Standard, the Subarctic Winter, and the Tropical models. I used a particularly interesting case of Sarin detection with the Subarctic Winter atmospheric model to illustrate the power of Modtran to predict subtle changes in Δ T with angle of elevation (AOE).
ABSTRACT
The objective of this effort is to provide guidance for the determination of spectral resolution for the passive remote detection of organic vapors. Target bands were modeled as Lorentzian bands. Several sensor models were used, including a detector-limited sensor model and a background-limited model.
An expression for the signal-to-noise ratio (SNR) was derived, and the SNR was computed for an SF(6) target band. The results show that substantial gains in sensitivity are possible if the conventional laboratory spectral resolution of 2 cm(-1) is reduced to 8 or even 16 cm(-1).
ABSTRACT
Different investigators have published different relationships for the same problem of predicting the signal-to-noise ratio for incoherent passive detection of infrared radiation. The results of two authors are compared, and a possible basis for the differences is explained.
ABSTRACT
There is a requirement for the controlled testing of passive infrared remote-sensing vapor detectors. The driving mechanism for the operation of these sensors is the small temperature difference ΔT that occurs between the target vapor and the background. Natural ΔT's, ranging from a fraction of a degree Kelvin to 20 K or more, have to be duplicated in the laboratory with the vapor contained in a cell. It is shown that the windows of the cell nonlinearly affect the measurements. A proposal is made for a new type of vapor cell, the ectocell, which effectively eliminates the window problems for differential measurements.
ABSTRACT
It is well known that dust clouds selectively absorb radiation in the 700-1300 cm(-1) atmospheric window region. Studies have shown that dust clouds are composed of the same minerals as surface soils, although in different proportion. We have examined seventy soil samples from a number of locations around the world to determine their compositions and spectral characteristics. The results indicate that there are five major components which selectively absorb radiation in the 700-1300 cm(-1) region. These are three clay minerals, silica, and calcium carbonate. Absorptivity coefficient spectra of representative soil samples are given.
