Passive acoustic threat detection in estuarine environments

Abstract

The Maritime Security Laboratory (MSL) at Stevens Institute of Technology supports research in a range of areas relevant to harbor security, including passive acoustic detection of underwater threats. The difficulties in using passive detection in an urban estuarine environment include intensive and highly irregular ambient noise and the complexity of sound propagation in shallow water. MSL conducted a set of tests in the Hudson River near Manhattan in order to measure the main parameters defining the detection distance of a threat: source level of a scuba diver, transmission loss of acoustic signals, and ambient noise. The source level of the diver was measured by comparing the diver's sound with a reference signal from a calibrated emitter placed on his path. Transmission loss was measured by comparing noise levels of passing ships at various points along their routes, where their distance from the hydrophone was calculated with the help of cameras and custom software. The ambient noise in the Hudson River was recorded under varying environmental conditions and amounts of water traffic. The passive sonar equation was then applied to estimate the range of detection. Estimations were done for a subset of the recorded noise levels, and we demonstrated how variations in the noise level, attenuation, and the diver's source level influence the effective range of detection. Finally, we provided analytic estimates of how an array improves upon the detection distance calculated by a single hydrophone.

Aliasing-Free Wideband Beamforming Using Sparse Signal Representation

“…; Q0 1; similarly, we use y n;k (p) to denote the kth subband (frequency) coefficient for the pth segment of the signal received at the nth channel. If the bandwidth of each subband is much smaller than its central frequency f k , the narrowband assumption applies, which enables the following equality [17]: y n;k (pT ) = Q01 q=0 e |2f n sin S q;k (pT ) (1) where c stands for the propagation velocity of the signal and m for the DOA of the qth target. 1 In the above equation, we have used the time of arrival at the first channel (n = 0) as a reference time and neglected the additive noise term just for the sake of notational ease.…”

“…Another example, given at the end of this paper, is a diver equipped with an open-circuit breathing set (scuba). The exhaling sound of the diver is so wideband that it can almost be viewed as white [1]. Wideband "aliasing-free" beamforming has been reported in, e.g., [2]- [5].…”

“…(1) 3 , (1) T , and (1) H represent conjugate, transpose and complex conjugate transpose (Hermitian), respectively. Calligraphic letters are used to denote sets.…”

Passive Sonar Target Detection Using Statistical Classifier and Adaptive Threshold

“…Chin et al performed Two-Pass Split-Windows (TPSW) algorithm and Neural Network to classify under water signal [5]. Borowski and et al in [6] investigated analysis under water signal in frequency domain. Abarahm performed non-Gaussian function for determines Detection Threshold (DT) in [7].…”

Signal Processing: Passive Acoustic in Fisheries and Marine Mammals

“…Measurement is done by using the software, and also to listen. Passive acoustic methods are also used by the military in developing the security system of the attacker under water in the estuary area by recording the sound generated from underwater divers [3]. Passive acoustics are not separated by their sound (Sound).…”