Aural Classification and Temporal Robustness

摘要

Active sonar systems are used to detect underwater manmade objects of interest (targets) that are too quiet to be reliably detected with passive sonar. In coastal waters, the performance of active sonar is degraded by false alarms caused by echoes returned from geological seabed structures (clutter) found in these shallow regions. To reduce false alarms, a method of distinguishing target echoes from clutter echoes is required. Research has demonstrated that perceptual signal features similar to those employed in the human auditory system can be used to automatically discriminate between target and clutter echoes, thereby improving sonar performance by reducing the number of false alarms. An active sonar experiment on the Malta Plateau was conducted during the Clutter07 sea trial and repeated during the Clutter09 sea trial. Broadband sources were used to transmit linear FM sweeps (600:3400 Hz) and a cardioid towed-array was used as the receiver. The dataset consists of over 95 000 pulse-compressed echoes returned from two targets and many geological clutter objects. These echoes are processed using an automatic classifier that quantifies the timbre of each echo using a number of perceptual signal features. Using echoes from 2007, the aural classifier is trained to establish a boundary between targets and clutter in the feature space. Temporal robustness is then investigated by testing the classifier on echoes from the 2009 experiment.