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Article

The Quadrature Method: A Novel Dipole Localisation Algorithm for Artificial Lateral Lines Compared to State of the Art

1
I-BioStat, Data Science Institute, Hasselt University, 3500 Hasselt, Belgium
2
Delft Center for Systems and Control, Delft University of Technology, 2628 CD Delft, The Netherlands
3
Bernoulli Institute of Mathematics, Computer Science and Artificial Intelligence, Faculty of Science and Engineering, University of Groningen, 9747 AG Groningen, The Netherlands
*
Authors to whom correspondence should be addressed.
Academic Editor: Enrico Meli
Sensors 2021, 21(13), 4558; https://doi.org/10.3390/s21134558
Received: 10 May 2021 / Revised: 17 June 2021 / Accepted: 28 June 2021 / Published: 2 July 2021
(This article belongs to the Section Sensing and Imaging)
The lateral line organ of fish has inspired engineers to develop flow sensor arrays—dubbed artificial lateral lines (ALLs)—capable of detecting near-field hydrodynamic events for obstacle avoidance and object detection. In this paper, we present a comprehensive review and comparison of ten localisation algorithms for ALLs. Differences in the studied domain, sensor sensitivity axes, and available data prevent a fair comparison between these algorithms from their original works. We compare them with our novel quadrature method (QM), which is based on a geometric property specific to 2D-sensitive ALLs. We show how the area in which each algorithm can accurately determine the position and orientation of a simulated dipole source is affected by (1) the amount of training and optimisation data, and (2) the sensitivity axes of the sensors. Overall, we find that each algorithm benefits from 2D-sensitive sensors, with alternating sensitivity axes as the second-best configuration. From the machine learning approaches, an MLP required an impractically large training set to approach the optimisation-based algorithms’ performance. Regardless of the data set size, QM performs best with both a large area for accurate predictions and a small tail of large errors. View Full-Text
Keywords: hydrodynamic imaging; dipole localisation; artificial lateral line; neural networks hydrodynamic imaging; dipole localisation; artificial lateral line; neural networks
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MDPI and ACS Style

Bot, D.M.; Wolf, B.J.; van Netten, S.M. The Quadrature Method: A Novel Dipole Localisation Algorithm for Artificial Lateral Lines Compared to State of the Art. Sensors 2021, 21, 4558. https://doi.org/10.3390/s21134558

AMA Style

Bot DM, Wolf BJ, van Netten SM. The Quadrature Method: A Novel Dipole Localisation Algorithm for Artificial Lateral Lines Compared to State of the Art. Sensors. 2021; 21(13):4558. https://doi.org/10.3390/s21134558

Chicago/Turabian Style

Bot, Daniël M., Ben J. Wolf, and Sietse M. van Netten. 2021. "The Quadrature Method: A Novel Dipole Localisation Algorithm for Artificial Lateral Lines Compared to State of the Art" Sensors 21, no. 13: 4558. https://doi.org/10.3390/s21134558

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