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

Dror Kipnis, Roee DIamant

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number8579234
Pages (from-to)702-706
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Issue number5
StatePublished - May 2019


  • Clustering
  • factor graphs
  • loopy belief propagation (LBP)
  • sea experiment
  • signal detection
  • underwater acoustics

All Science Journal Classification (ASJC) codes

  • Geotechnical Engineering and Engineering Geology
  • Electrical and Electronic Engineering


Dive into the research topics of 'A Factor-Graph Clustering Approach for Detection of Underwater Acoustic Signals'. Together they form a unique fingerprint.

Cite this