This paper provides a statistical analysis method for detecting and discriminating different seismic activity sources such
as humans, animals, and vehicles using their seismic signals. A five-step process is employed for this purpose: (1) a set
of signals with known seismic activities are utilized to verify the algorithms; (2) for each data file, the vibration signal is
segmented by a sliding-window and its noise is reduced; (3) a set of features is extracted from each window of the signal
which captures its statistical and spectral properties. This set is formed as an array and is called a feature array; (4) a
portion of the labeled feature arrays are utilized to train a classifier for discriminating different types of signals; and
(5) the rest of the labeled feature arrays are employed to test the performance of the developed classifier. The results
indicate that the classifier achieves probability of detection (pd) above 95% and false alarm rate (pfa) less than 1%.
The Range Test Validation System (RTVS) includes a constellation of five AIRIS-WAD standoff multispectral sensors
oriented around a 1000×1000 meter truth box at a range of 2700 meters. Column density data derived from these sensors
is transmitted in real-time to a command post using a wireless network. The data is used with computed tomographic
methods to produce 3-D cloud concentration profiles for chemical clouds traversing the box. These concentration
profiles are used to provide referee capability for the evaluation of both point and standoff sensors under test. The system
has been used to monitor chemical agent simulants released explosively as well as continuously through specialized
stacks. The system has been demonstrated to accurately map chemical clouds with concentrations as low as 0.5 mg/m3 at
spatial and temporal resolutions of 6 meters and 3 seconds.1 Data products include geo-referenced cloud mass centroids
and boundaries as well as total cloud mass.
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