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Title: Learning a detection map for a network of unattended ground sensors.

We have developed algorithms to automatically learn a detection map of a deployed sensor field for a virtual presence and extended defense (VPED) system without apriori knowledge of the local terrain. The VPED system is an unattended network of sensor pods, with each pod containing acoustic and seismic sensors. Each pod has the ability to detect and classify moving targets at a limited range. By using a network of pods we can form a virtual perimeter with each pod responsible for a certain section of the 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 installed at a site by a mobile deployment unit (MDU). The MDU will wear a GPS unit, so the system not only knows when it can detect the MDU, but also the MDU's location. In this paper, we demonstrate how to handle anisotropic sensor-configurations, geography, and soil conditions.
Authors:
;
Publication Date:
OSTI Identifier:
988513
Report Number(s):
SAND2010-1327C
TRN: US201018%%535
DOE Contract Number:
AC04-94AL85000
Resource Type:
Conference
Resource Relation:
Conference: Proposed for presentation at the SPIE Defense, Security, and Sensing held June 27-30, 2010 in Salt Lake City, UT.
Research Org:
Sandia National Laboratories
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; ACOUSTICS; ALGORITHMS; DETECTION; GEOGRAPHY; GLOBAL POSITIONING SYSTEM; LEARNING; PERFORMANCE; SECURITY; SOILS; TARGETS