Publication

Training submerged source detection for a 2D fluid flow sensor array with Extreme Learning Machines

Wolf, B. & van Netten, S., 15-Mar-2019, Eleventh International Conference on Machine Vision (ICMV 2018). SPIE.Digital Library, Vol. 11041. p. 1104126 8 p.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

An array of fluid flow sensors can be used to detect and track underwater objects via the fluid flow field these objects create. The sensed flows combine to a spatio-temporal velocity profile, which can be used to solve the inverse problem; determining the relative position and orientation of a moving source via a trained model. In this study, two training strategies are used: simulated data resulting from continuous motion in a path and from vibratory motion at discrete locations on a grid. Furthermore, we investigate two sensing modalities found in literature: 1D and 2D sensitive flow sensors; all while varying the sensor detection threshold via a noise level. Results show that arrays with 2D sensors outperform those with 1D sensors, especially near and next to the sensor array. On average, the path method outperforms the grid method with respect to estimating the location and orientation of a source.
Original languageEnglish
Title of host publicationEleventh International Conference on Machine Vision (ICMV 2018)
PublisherSPIE.Digital Library
Pages1104126
Number of pages8
Volume11041
Publication statusPublished - 15-Mar-2019
EventEleventh International Conference on Machine Vision (ICMV 2018), 2018, Munich, Germany - Munich, Germany
Duration: 1-Nov-20183-Nov-2018

Conference

ConferenceEleventh International Conference on Machine Vision (ICMV 2018), 2018, Munich, Germany
CountryGermany
CityMunich
Period01/11/201803/11/2018

Event

Eleventh International Conference on Machine Vision (ICMV 2018), 2018, Munich, Germany

01/11/201803/11/2018

Munich, Germany

Event: Conference

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