Publications

A major goal of next-generation physical protection systems is to extend defenses far beyond the usual outer-perimeter-fence boundaries surrounding protected facilities. Mitigation of nuisance alarms is among the highest priorities. A solution to this problem is to create a robust capability to Automatically Recognize Malicious Indicators of intruders. In extended defense applications, it is not enough to distinguish humans from all other potential alarm sources as human activity can be a common occurrence outside perimeter boundaries. Our approach is unique in that it employs a stimulus to determine a malicious intent indicator for the intruder. The intruder's response to the stimulus can be used in an automatic reasoning system to decide the intruder's intent. ©2010 IEEE.

Abstract not provided.

This paper explores the possibility of separating and classifying remotely-sensed multispectral data from rocks and minerals onto seven geological rock-type groups. These groups are extracted from the general categories of metamorphic, igneous and sedimentary rocks. The study is performed under ideal conditions for which the data is generated according to laboratory hyperspectral data for the members, which are, in turn, passed through the Multi-spectral Thermal Imager (MTI) filters yielding 15 bands. The main challenge in separability is the small size of the training data sets, which initially did not permit direct application of Bayesian decision theory. To enable Bayseian classification, the original training data is linearly perturbed with the addition minerals, vegetation, soil, water and other valid impurities. As a result, the size of the training data is significantly increased and accurate estimates of the covariance matrices are achieved. In addition, a set of reduced (five) linearly-extracted canonical features that are optimal in providing the most important information about the data is determined. An alternative nonlinear feature-selection method is also employed based on spectral indices comprising a small subset of all possible ratios between bands. By applying three optimization strategies, combinations of two and three ratios are found that provide reliable separability and classification between all seven groups according to the Bhattacharyya distance. To set a benchmark to which the MTI capability in rock classification can be compared, an optimization strategy is performed for the selection of optimal multispectral filters, other than the MTI filters, and an improvement in classification is predicted.

Robotic vehicles that navigate autonomously are hindered by unnecessary avoidance of soft obstacles, and entrapment by potentially avoidable obstacles. Existing sensing technologies fail to reliably distinguish hard obstacles from soft obstacles, as well as impassable thickets and other sources of entrapment. Automated materials classification through advanced sensing methods may provide a means to identify such obstacles, and from their identity, to determine whether they must be avoided. Multi- and hyper-spectral electro-optic sensors are used in remote sensing applications to classify both man-made and naturally occurring materials on the earth's surface by their reflectance spectra. The applicability of this sensing technology to obstacle identification for autonomous ground vehicle navigation is the focus of this report. The analysis is restricted to system concepts in which the multi- or hyper-spectral sensor is on-board the ground vehicle, facing forward to detect and classify obstacles ahead of the vehicle. Obstacles of interest include various types of vegetation, rocks, soils, minerals, and selected man-made materials such as paving asphalt and concrete.