Investigation of novel spectral and wavelet statistics for UGS-based intrusion detection

Ranga Narayanaswami; Avinash Gandhe; Anastasia Tyurina; Michael McComas; Raman K. Mehra

doi:10.1117/12.918694

24 May 2012 Investigation of novel spectral and wavelet statistics for UGS-based intrusion detection

Ranga Narayanaswami, Avinash Gandhe, Anastasia Tyurina, Michael McComas, Raman K. Mehra

Proceedings Volume 8388, Unattended Ground, Sea, and Air Sensor Technologies and Applications XIV; 83880N (2012) https://doi.org/10.1117/12.918694
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

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.

Citation Download Citation

Ranga Narayanaswami, Avinash Gandhe, Anastasia Tyurina, Michael McComas, and Raman K. Mehra "Investigation of novel spectral and wavelet statistics for UGS-based intrusion detection", Proc. SPIE 8388, Unattended Ground, Sea, and Air Sensor Technologies and Applications XIV, 83880N (24 May 2012); https://doi.org/10.1117/12.918694

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