A multitask convolutional neural network (CNN) is trained to localize the instantaneous position of a motorboat throughout its transit past a wide aperture linear array of hydrophones located 1 m above the sea floor in water 20 m deep. A cepstrogram database for each hydrophone and a cross-correlogram database for each pair of adjacent hydrophones are compiled for multiple motorboat transits. Cepstrum-based and correlation-based feature vectors (along with ground-truth source bearing and range data) form the inputs to train three CNNs so that they can predict the instantaneous source range and bearing for other "unseen" motorboat transits.
View Article and Find Full Text PDFWhen configured as a wide aperture array, only three hydrophones are required to localize dolphin sonar transmissions with unprecedented precision, even when the underwater sound scene of their natural habitat is complicated by many of them emitting echolocation "click" signals at the same time. Given the sensor position coordinates and speed of sound travel, the passive ranging by the wavefront curvature algorithm estimates the source range and bearing, using range difference measurements between signals, arriving at two adjacent pairs of widely spaced sensors. If the sensor positions are not strictly collinear, then the source range estimates are biased.
View Article and Find Full Text PDFWhen a broadband source of radiated noise transits past a fixed hydrophone, a Lloyd's mirror constructive/destructive interference pattern can be observed in the output spectrogram. By taking the spectrum of a (log) spectrum, the power cepstrum detects the periodic structure of the Lloyd's mirror fringe pattern by generating a sequence of pulses located at the fundamental quefrency and its multiples. The fundamental quefrency, which is the reciprocal of the frequency difference between adjacent destructive interference fringes, equates to the multipath delay time.
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