Interception of Bio-Mimicking Underwater Acoustic Communications Signals

We describe a biomimicking interception scheme tailored to Underwater Acoustic Communications (UAC), which aims at separating authentic and biomimicking signals. Our interceptor leverages the expected stability of the biomimicking sources, as opposed to vocalizations by marine fauna, which are expected to move fast and rapidly change orientation. Consequently, the channel impulse response (CIR) of the link between a receiver and a biomimicking source is expected to be much more stable than those corresponding to actual vocalizations. We quantify this stability by testing the randomness of the representation of the CIRs. The latter are represented by two similarity metrics: the cross-correlation and the sample entropy between adjacent CIRs features. We offered two interception measures: 1) testing the similarity between a Gaussian distribution and the distribution of the similarity measures using the Kullback–Leibler divergence (KLD) criteria for quantification, and 2) the minimum number of clusters to effectively segment the similarity measures as a point cloud. Results from simulations for artificial signals mimicking dolphin whistles and the outcomes of a lake trial demonstrate the effectiveness of our biomimicking interceptor.