  A cost effective approach to remote monitoring of protected areas such as
marine reserves and restricted naval waters is to use passive sonar to detect,
classify, localize, and track marine vessel activity (including small boats and
autonomous underwater vehicles). Cepstral analysis of underwater acoustic data
enables the time delay between the direct path arrival and the first multipath
arrival to be measured, which in turn enables estimation of the instantaneous
range of the source (a small boat). However, this conventional method is
limited to ranges where the Lloyd's mirror effect (interference pattern formed
between the direct and first multipath arrivals) is discernible. This paper
proposes the use of convolutional neural networks (CNNs) for the joint
detection and ranging of broadband acoustic noise sources such as marine
vessels in conjunction with a data augmentation approach for improving network
performance in varied signal-to-noise ratio (SNR) situations. Performance is
compared with a conventional passive sonar ranging method for monitoring marine
vessel activity using real data from a single hydrophone mounted above the sea
floor. It is shown that CNNs operating on cepstrum data are able to detect the
presence and estimate the range of transiting vessels at greater distances than
the conventional method.
