Three-dimensional multi-source localization of underwater objects using convolutional neural networks for artificial lateral lines

Ben J Wolf*, Jos van de Wolfshaar, Sietse M van Netten

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)
106 Downloads (Pure)


This research focuses on the signal processing required for a sensory system that can simultaneously localize multiple moving underwater objects in a three-dimensional (3D) volume by simulating the hydrodynamic flow caused by these objects. We propose a method for localization in a simulated setting based on an established hydrodynamic theory founded in fish lateral line organ research. Fish neurally concatenate the information of multiple sensors to localize sources. Similarly, we use the sampled fluid velocity via two parallel lateral lines to perform source localization in three dimensions in two steps. Using a convolutional neural network, we first estimate a two-dimensional image of the probability of a present source. Then we determine the position of each source, via an automated iterative 3D-aware algorithm. We study various neural network architectural designs and different ways of presenting the input to the neural network; multi-level amplified inputs and merged convolutional streams are shown to improve the imaging performance. Results show that the combined system can exhibit adequate 3D localization of multiple sources.

Original languageEnglish
Article number20190616
JournalJournal of the Royal Society Interface
Issue number162
Publication statusPublished - Jan-2020


  • convolutional neural network
  • hydrodynamic imaging
  • inverse problem
  • lateral line
  • sensor array
  • source localization
  • FISH

Cite this