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  • Research Article
  • Open Access

Flow Vision for Autonomous Underwater Vehicles via an Artificial Lateral Line

EURASIP Journal on Advances in Signal Processing20102011:806406

  • Received: 15 June 2010
  • Accepted: 23 November 2010
  • Published:


Most fish have the capability of sensing flows and nearby movements even in dark or murky conditions by using the lateral line organs. This enables them to perform a variety of underwater activities, such as localizing prey, avoiding predators, navigating in narrow spaces, and schooling. To emulate this capability for Autonomous Underwater Vehicles, we developed an artificial lateral line using an array of Micro-Electro-Mechanical-Systems (MEMS) flow sensors. The signals collected via the artificial lateral line are then processed by an adaptive beamforming algorithm developed from Capon's method. The system produces 3D images of source locations for different hydrodynamic activities, including the vibration of a dipole source and the movement of a tail-flicking crayfish. A self-calibration algorithm provides the capability of self-adaptation to different environments. Lastly, we give a Cramer-Rao bound on the theoretical performance limit which is consistent with experimental results.


  • Source Location
  • Lateral Line
  • Performance Limit
  • Full Article
  • Autonomous Underwater Vehicle

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Authors’ Affiliations

Department of Electrical and Computer Engineering, University of Illinois at Urbana Champaign, 1308 West Main Street, Urbana, IL 61801, USA
Department of Engineering, University of Texas at Brownsville, 80 Fort Brown, Brownsville, TX 78520, USA
Department of Mechanical Engineering, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA


© Nam Nguyen et al. 2011

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.