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.
Measurements of the reverberation time series are made at frequencies of 8, 10, and 12 kHz, and the corresponding acoustic bottom backscattering strengths are estimated as functions of grazing angle. The experiment was conducted in the western continental shelf of India (off Kerala) in water depth of ∼61 m where hard sandy sediments of biogenic origin are predominant. The average values of two-dimensional (2D) spectral strength (w2) and exponent (γ2) of seafloor roughness are obtained by inverting bottom backscattering strength data with the help of a scattering model, utilizing the genetic algorithm method. Measurements of one-dimensional interface roughness height are also carried out using a single beam echosounder to analyze the variability of bottom roughness in terms of spatial frequency. The spectral parameters estimated from roughness height measurements are compared to that obtained from inversion results. The 2D spectral strength and exponent of seafloor roughness estimated from the two methods agree with each other and are consistent with the typical values associated with sandy sediments.
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