Active sonar systems are used to detect underwater man-made objects of interest (targets) that are too quiet to be reliably detected with passive sonar. Performance of active sonar can be degraded by false alarms caused by echoes returned from geological seabed structures (clutter) in shallow regions. To reduce false alarms, a method of distinguishing target echoes from clutter echoes is required. Research has demonstrated that perceptual-based signal features similar to those employed in the human auditory system can be used to automatically discriminate between target and clutter echoes, thereby reducing the number of false alarms and improving sonar performance. An active sonar experiment on the Malta Plateau in the Mediterranean Sea was conducted during the Clutter07 sea trial and repeated during the Clutter09 sea trial. The dataset consists of more than 95,000 pulse-compressed echoes returned from two targets and many geological clutter objects. These echoes were 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 was trained to establish a boundary between targets and clutter in the feature space. Temporal robustness was then investigated by testing the classifier on echoes from the 2009 experiment.

Military sonar systems must detect and classify submarine threats at ranges safely outside their circle of attack. However, in littoral environments, echoes from geological features (clutter) are frequently mistaken for targets of interest, resulting in degraded performance. 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. [J. Acoust. Soc. Am. 122, 1502–1517 (2007)] The present work examines the temporal robustness of the aural classifier using data from two field trials: the first in 2007 and the second in 2009. The experiments were conducted on the Malta Plateau using a cardioid towed-array receiver, and a broadband source transmitting linear FM sweeps from 600–3500 Hz. The data set consists of hundreds of pulse-compressed echoes from several surrogate targets and geological clutter objects. The echoes are examined using an automatic classifier that processes each echo to extract perceptual features. Each echo is classified as target or clutter based on the position vector formed by these features. The classifier establishes a boundary between clutter and target echoes in the feature space using the 2007 experiment. Temporal robustness is investigated by testing the classifier on echoes from the 2009 experiment. In this work, the experiments are reviewed and initial results are presented.

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Military sonars must detect, localize, classify, and track submarine threats from distances safely outside their circle of attack. However, conventional pulsed active sonars (PAS) have duty cycles on the order of 1% which means that 99% of the time, the track is out of date. In contrast, continuous active sonars (CAS) have a 100% duty cycle, which enables continuous updates to the track. This should significantly improve tracking performance. However, one would typically want to maintain the same bandwidth for a CAS system as for the PAS system it might replace. This will provide a significant increase in the time-bandwidth product, but may not produce the increase in gain anticipated if there are coherence limitations associated with the acoustic channel. To examine the impact of the acoustic channel on the gain for the two pulse types, an experiment was conducted as part of the Target and Reverberation Experiment (TREX) in May 2013 using a moored active sonar and three passive acoustic targets, moored at ranges from 2 to 6 km away from the sonar. In this paper, preliminary results from the experiment will be presented. [Work supported by the U.S. Office of Naval Research.]

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