This research investigates the classification of battlespace detonations, specifically the determination of munitions type and size using temporal and spectral features from near-infrared (NIR) and visible wavelength imagers. Key features from the time dependence of fireball size are identified for discriminating various types and sizes of detonation flashes. The five classes include three weights of trinitrotoluene (TNT) and two weights of an enhanced mixture, all of which are uncased and detonated with 10% C-4. Using Fisher linear discriminant techniques, these features are projected onto a line such that the projected points are maximally clustered for the different classes of detonations. Bayesian decision boundaries for classification are then established on class-conditional probability densities and are tested using independent test data. Feature saliency and stability are determined by selecting those features that best discriminate while requiring low variations in class-conditional probability densities and high performance in independent testing. Given similar conditions, the most important and stable feature is the time to the maximum fireball area in the near-infrared wavelength band (0.6 to 1.7 microns). This feature correctly discriminates between TNT and ENE about 90% of the time, whether weight is known or not. The associated class-conditional probability densities separate the two classes with a Fisher ratio of 2.9±0.3 and an area under the receiver operating characteristic, A<sub>ROC</sub>, of 0.992. Also, t<sub>mp</sub> achieves approximately 54% success rate at discerning both weight and type.
This research investigates the classification of battlespace detonations, specifically the determination of munitions type and size using image features from an infrared wavelength camera. Experimental data are collected for the detonation of several types of conventional munitions with different high explosive materials and different weights. Key features are identified for discriminating various types and sizes of detonation flashes. These features include statistical parameters derived from the time dependence of fireball size. Using Fisher linear discriminant techniques, these features are projected onto a line such that the projected points are maximally clustered for different classes of detonations. Bayesian decision boundaries for classification are then determined.
Infrared emissions from the detonation of three bomb types and four weights in a series of 56 events were recorded by a Fourier transform interferometer in the mid-IR (1800-6000 cm<sup>-1</sup>) at temporal and spectral resolutions of 0.047 s and 16 cm<sup>-1</sup>, respectively. Spectrally-resolved time profiles from two representative detonation events were selected for this study. Two parameterized, empirical functions adequately represent the temporal signatures taken from four spectral bands in which atmospheric attenuation losses are small. Each function is the sum of either two or three exponential terms modulated by delayed switching functions. The number of exponential terms required to fit each temporal profile depends on the explosive and varies with the frequency. The dimensionality of the spectrally-resolved temporal signatures is significantly reduced by establishing these characteristic timescales. Such feature extraction is critical for proposed event classification schemes.
To investigate the possibility of battlespace characterization, including the ability to classify munitions type and size, experimental data has been collected remotely from ground-based sensors, processed, and analyzed for several conventional munitions. The spectral, temporal and spatial infrared signatures from bomb detonations were simultaneously recorded using a Bomem MR157 Fourier Transform Infrared Spectrometer and an Indigo Systems Alpha Near-Infrared camera. Three different high explosive materials at three different quantities each were examined in one series of field studies. The FTIR spectra were recorded at 4 cm<sup>-1</sup> spectral resolution and 123-ms temporal resolution using both HgCdTd (500-6000 cm<sup>-1</sup>) and InSb (1800-6000 cm<sup>-1</sup>) detectors. Novel key features have been identified that will aid in discriminating various types and sizes of flashes. These features include spectral dependent projections of one event's temporal data onto another event's temporal data, time dependence of the fireball size, ratios of specific integrated bands, and spectral dependence of temporal fit constants. Using Fisher discrimination and principal component techniques these features are projected onto a line that maximizes the differences in the classes of flashes and then identify the Bayesian decision boundaries for classification.
We present a holographic lidar system, designed to give complete temperature profiles of the atmosphere. The lidar uses rotational Raman scattering (RRS) from 0-30km and Rayleigh scattering (RS) from 30-100km. The main feature of our lidar is a holographic optical element (HOE) which allows individual lines in the nitrogen rotational RRS to be extracted with high efficiency, along with the Rayleigh return. Due to the effectiveness of the holographic filters we have constructed, our lidar can achieve levels of performance far above existing systems using narrowband filters. The system requires no calibration to radiosondes and has nominal susceptibility to environmental fluctuations.