  Abundant in coastal areas, sea turtles are affected by high-intensity
acoustic anthropogenic sounds. In this paper, we offer a pattern analysis-based
detection approach to serve as a warning system for the existence of nearby sea
turtles. We focus on the challenge of overcoming the low signal-to-clutter
ratio (SCR) caused by reverberations. Assuming that, due to low SCR, target
reflections within the point cloud are received in groups, our detector
searches for patterns through clustering to identify possible 'blobs' in the
point cloud of reflections, and to classify them as either clutter or a target.
Our unsupervised clustering is based on geometrical and spectral constraints
over the blob's member relations. In turn, the classification of identified
blobs as either a target or clutter is based on features extracted from the
reflection pattern. To this end, assuming reflections from a sea turtle are
stable but include spectral diversity due to distortions within the turtles
body, we quantify the stability of the blob's members and their spectral
entropy. We test our detector in both modeled simulations, and at sea, for the
detection of sea turtles released after rehabilitation. The results show
robustness to highly-fluctuating target intensity and ability to detect at low
SCR.
