Fusion of acoustic measurements with video surveillance for estuarine threat detection

Bunin, Barry; Sutin, Alexander; Kamberov, G.; Roh, Heui‐Seol; Luczynski, Bart; Burlick, Matt

doi:10.1117/12.779176

Cited by 10 publications

(6 citation statements)

References 13 publications

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“…The second graph represents the development of the y-coordinate in comparison the measurement. The two graphs shows that the UKF starts to perform well after few couple of iterations (10) and the margin in errors between the estimation and the measurement is very tiny. …”

Section: Application Of the Ukf To The Acoustic Localizationmentioning

confidence: 92%

“…If the video controls the particle diversity at low signal-to-noise (SNR) region of the acoustics, the overall target tracking performance will be improved. While in [10] the combination of the underwater acoustic measurements and video surveillance was suggested, a specialized prototype video system was developed to identify, video-capture, and map surface ships in a sector of the estuary. The combination of acoustic signal with video data for different kinds of ships enabled estimation of sound attenuation in a wide frequency band and ships classification.…”

Section: Introductionmentioning

confidence: 99%

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Acoustic Detection and Localization Enhanced by Video Analysis

Azzam

Aouf

2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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Acoustic data have already been used for security (and military) applications such as sniper detection and gun firing localization, pedestrians and cars detection and tracking. However, in some situations, the measurement results of the acoustic data (range and bearing) alone appear to be insufficient to locate accurately targets especially because of the presence of the background noise or due to the effect of the reverberation on estimating the true time delay of arrival of the acoustic wave. Furthermore, the influence of the errors related to the sensors synchronisation is also negatively impacting. In this work, a recursive technique based on Unscented Kalman Filter (UKF) will be used to estimate the position of the acoustic source, in addition to using video data to improve the accuracy of the acoustic measurement by fusing the two types of data (acoustic and video). This combination showed through experimental results improvement in terms of target localisation accuracy.

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Section: Application Of the Ukf To The Acoustic Localizationmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Acoustic Detection and Localization Enhanced by Video Analysis

Azzam

Aouf

2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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“…Here we present the results of one of the Hudson River experiments. The test area and Stevens vessels are the same as those used in previous experiments [12][13][14] . The depth of the test area was approximately 3 meters.…”

Section: Diver Acoustic Signature and Source Levelmentioning

confidence: 99%

“…MSL took measurements of sound attenuation using the noise produced by passing ships. As in Section 2.1, each ship's location and distance from the hydrophone were obtained from tracking software 14 . The transmission loss in shallow water can be described by the equation,…”

Section: Transmission Lossmentioning

confidence: 99%

“…Acoustic signals, including those produced by a diver, can be detected by a fiber-optic hydrophone array that was developed in the United Kingdom for acoustic surveillance of the littoral 11 . Stevens Institute of Technology conducted research on various physical phenomena in the complex conditions of an actual urban estuarine environment, introducing new methods of securing ports [12][13][14][15] . This paper presents the result of recent research in this area.…”

Section: Introductionmentioning

confidence: 99%

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Passive acoustic threat detection in estuarine environments

et al. 2008

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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.

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