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Underwater Positioning Based on an Artificial Lateral Line and a Generalized Regression Neural Network 被引量:8

Underwater Positioning Based on an Artificial Lateral Line and a Generalized Regression Neural Network
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摘要 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 gener- alized 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 in- creasing the number of sensors utilized can help improve the positioning accuracy. 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 gener- alized 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 in- creasing the number of sensors utilized can help improve the positioning accuracy.
出处 《Journal of Bionic Engineering》 SCIE EI CSCD 2018年第5期883-893,共11页 仿生工程学报(英文版)
基金 The author appreciates the support of the National Natural Science Foundation of China (Grant Nos. 51675528 and 51605482 as well as the National Key R&D Program of China (Grant No. 2016YFF0203400). The author also thanks Kehong Lv and Peng Yang for guiding in the design of the experimental platform. Besides, the author thanks Qin Wang and Bailiang Chen for assisting in the fabrication of the sensor array and the experimental platform.
关键词 lateral line underwater positioning generalized regression neural network BIONICS lateral line underwater positioning generalized regression neural network bionics
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