A Factor-Graph Clustering Approach for Detection of Underwater Acoustic Signals

We address the challenge of detecting an arbitrary-shaped underwater acoustic signal. Instead of setting a detection threshold, which due to noise transients may result in a high false alarm rate (FAR), our method classifies each measured sample as either 'noise' or 'signal.' Utilizing a priori knowledge of only the minimal duration of the signal, the decision is made using loopy belief propagation over a factor graph. Numerical simulations and sea experimental results show that our scheme achieves a favorable tradeoff between the Recall and FAR, and noise robustness, which far exceeds that of benchmark schemes.