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.

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.

We have developed algorithms for a virtual presence and extended defense (VPED) system that automatically learns the detection map of a deployed sensor field without a-priori knowledge of the local terrain. The VPED system is a network of sensor pods, with each pod containing acoustic and seismic sensors. Each pod has a limited detection range, but a network of pods can form a virtual perimeter. The site's geography and soil conditions can affect the detection performance of the pods. Thus a network in the field may not have the same performance as a network designed in the lab. To solve this problem we automatically estimate a network's detection performance as it is being constructed. We demonstrate results using simulated and real data. © 2009 IEEE.