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Proceedings Paper

Investigation of novel spectral and wavelet statistics for UGS-based intrusion detection
Author(s): Ranga Narayanaswami; Avinash Gandhe; Anastasia Tyurina; Michael McComas; Raman K. Mehra
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Paper Abstract

Seismic Unattended Ground Sensors (UGS) are low cost and covert, making them a suitable candidate for border patrol. Current seismic UGS systems use cadence-based intrusion detection algorithms and are easily confused between humans and animals. The poor discrimination ability between humans and animals results in missed detections as well as higher false (nuisance) alarm rates. In order for seismic UGS systems to be deployed successfully, new signal processing algorithms with better discrimination ability between humans and animals are needed. We have characterized the seismic signals using frequency domain and time-frequency domain statistics, which improve the discrimination between humans, animals and vehicles.

Paper Details

Date Published: 25 May 2012
PDF: 9 pages
Proc. SPIE 8388, Unattended Ground, Sea, and Air Sensor Technologies and Applications XIV, 83880N (25 May 2012); doi: 10.1117/12.918694
Show Author Affiliations
Ranga Narayanaswami, Scientific Systems Co., Inc. (United States)
Avinash Gandhe, Scientific Systems Co., Inc. (United States)
Anastasia Tyurina, Scientific Systems Co., Inc. (United States)
Michael McComas, Scientific Systems Co., Inc. (United States)
Raman K. Mehra, Scientific Systems Co., Inc. (United States)


Published in SPIE Proceedings Vol. 8388:
Unattended Ground, Sea, and Air Sensor Technologies and Applications XIV
Edward M. Carapezza, Editor(s)

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