Paper
12 May 2006 Feature based passive acoustic detection of underwater threats
Rustam Stolkin, Alexander Sutin, Sreeram Radhakrishnan, Michael Bruno, Brian Fullerton, Alexander Ekimov, Michael Raftery
Author Affiliations +
Abstract
Stevens Institute of Technology is performing research aimed at determining the acoustical parameters that are necessary for detecting and classifying underwater threats. This paper specifically addresses the problems of passive acoustic detection of small targets in noisy urban river and harbor environments. We describe experiments to determine the acoustic signatures of these threats and the background acoustic noise. Based on these measurements, we present an algorithm for robustly discriminating threat presence from severe acoustic background noise. Measurements of the target's acoustic radiation signal were conducted in the Hudson River. The acoustic noise in the Hudson River was also recorded for various environmental conditions. A useful discriminating feature can be extracted from the acoustic signal of the threat, calculated by detecting packets of multi-spectral high frequency sound which occur repetitively at low frequency intervals. We use experimental data to show how the feature varies with range between the sensor and the detected underwater threat. We also estimate the effective detection range by evaluating this feature for hydrophone signals, recorded in the river both with and without threat presence.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Rustam Stolkin, Alexander Sutin, Sreeram Radhakrishnan, Michael Bruno, Brian Fullerton, Alexander Ekimov, and Michael Raftery "Feature based passive acoustic detection of underwater threats", Proc. SPIE 6204, Photonics for Port and Harbor Security II, 620408 (12 May 2006); https://doi.org/10.1117/12.663651
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CITATIONS
Cited by 36 scholarly publications and 2 patents.
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KEYWORDS
Acoustics

Signal to noise ratio

Signal detection

Interference (communication)

Electronic filtering

Environmental sensing

Linear filtering

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