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Proceedings Paper

Perimeter intrusion detection and intruder classification
Author(s): James F. Smith
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Paper Abstract

A fuzzy logic perimeter intrusion classification algorithm (FLPICA) has been developed to determine when intruders have crossed a perimeter and to classify the type of intrusion. The FLPICA works in real-time and makes its decisions based on data from single or multiple geophones. Extensive discussions of some of the fuzzy decision trees and fuzzy membership functions making up the algorithm are provided. The geophones can be one to three axes geophones, i.e., they can provide information in only one dimension or three. The FLPICA uses various signal processing algorithms to extract the quantities that facilitate decisions. The parameters that are extracted in real time from the data are the cadence of walkers, runners, jumpers, etc.; the bearing of intruders; power measures for the signal; and the signal's kurtosis. The FLPICA is applicable to many different environments and can be retrained as needed. It is based on rules born of human expertise. The FLPICA is applicable to many different scenarios, e.g., classifying intruders as walkers, runners, creepers, orbiters, jumpers, vehicles, animals, etc. It can also make a declaration as to the threat status of the intruder. The FLPICA can separate the signal of a human intruder on foot from those of vehicles and other noise sources. Examples where the intruder exhibits the behaviors of walkers, runners, creepers and orbiters are provided. Theoretical and simulation results are discussed.

Paper Details

Date Published: 11 May 2009
PDF: 12 pages
Proc. SPIE 7336, Signal Processing, Sensor Fusion, and Target Recognition XVIII, 73361I (11 May 2009); doi: 10.1117/12.819895
Show Author Affiliations
James F. Smith, Naval Research Lab. (United States)

Published in SPIE Proceedings Vol. 7336:
Signal Processing, Sensor Fusion, and Target Recognition XVIII
Ivan Kadar, Editor(s)

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