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, 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 trade-off between the recall and false alarm rates, and noise robustness which far exceeds that of benchmark schemes.