[2018-Vol.15-Issue 5]Underwater Positioning Based on an Artificial Lateral Line and a Generalized Regression Neural Network
Time: 2018-09-28 14:52  Click:47

Journal of Bionic Engineering

September 2018, Volume 15, Issue 5, pp 883–893

Xiande Zheng, Yong Zhang, Mingjiang Ji, Ying Liu, Xin Lin, Jing QiuEmail author, Guanjun LiuEmail author

1. Science and Technology on Integrated Logistics Support Laboratory, National University of Defense Technology, Changsha, China

2. College of Intelligence Science, National University of Defense Technology, Changsha, China

   Abstract

Taking advantage of the lateral line organ, fish can navigate, feed, and avoid predators and obstacles by sensing surrounding flow fields. The lateral line organ provides an important reference for the development of new underwater detection technology. Inspired by the lateral line organ, in this paper, for the sake of localizing the target dipole source in three-dimensional underwater space, an artificial lateral line consisting of nine underwater pressure sensors forming a cross-shaped sensor array is applied. Combined with the method of generalized regression neural network, which is suitable for solving nonlinear pattern recognition problems, a corresponding experimental platform has been built to sample data for training the neural network from a 12 cm by 12 cm by 24 cm cuboid space. The experimental results indicate that the cross-shaped artificial lateral line can localize the target dipole source two body-lengths away. The well-performing perceptual distance is below 13 cm away from the sensing array. Moreover, decreasing the data sampling interval and increasing the number of sensors utilized can help improve the positioning accuracy.

Keywords

lateral line   underwater positioning   generalized regression neural network   bionics 

Full text is available at : https://link.springer.com/article/10.1007/s42235-018-0075-z

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