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dc.contributor.authorTanveer, Muhammad Hassanen
dc.contributor.authorThomas, Antonyen
dc.contributor.authorAhmed, Waqaren
dc.contributor.authorZhu, Hongxiaoen
dc.date.accessioned2021-06-24T14:59:18Zen
dc.date.available2021-06-24T14:59:18Zen
dc.date.issued2021-06-18en
dc.identifier.citationTanveer, M.H.; Thomas, A.; Ahmed, W.; Zhu, H. Estimate the Unknown Environment with Biosonar Echoes—A Simulation Study. Sensors 2021, 21, 4186.en
dc.identifier.urihttp://hdl.handle.net/10919/103990en
dc.description.abstractUnmanned aerial vehicles (UAVs) have shown great potential in various applications such as surveillance, search and rescue. To perform safe and efficient navigation, it is vitally important for a UAV to evaluate the environment accurately and promptly. In this work, we present a simulation study for the estimation of foliage distribution as a UAV equipped with biosonar navigates through a forest. Based on a simulated forest environment, foliage echoes are generated by using a bat-inspired bisonar simulator. These biosonar echoes are then used to estimate the spatial distribution of both sparsely and densely distributed tree leaves. While a simple batch processing method is able to estimate sparsely distributed leaf locations well, a wavelet scattering technique coupled with a support vector machine (SVM) classifier is shown to be effective to estimate densely distributed leaves. Our approach is validated by using multiple setups of leaf distributions in the simulated forest environment. Ninety-seven percent accuracy is obtained while estimating thickly distributed foliage.en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.publisherMDPIen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectUnmanned aerial vehiclesen
dc.subjectsimulationen
dc.subjectbio-inspired sensingen
dc.subjectbiosonaren
dc.subjectwavelet scatteringen
dc.subjectsupport vector machineen
dc.titleEstimate the Unknown Environment with Biosonar Echoes—A Simulation Studyen
dc.typeArticle - Refereeden
dc.date.updated2021-06-24T14:12:02Zen
dc.description.versionPublished versionen
dc.contributor.departmentStatisticsen
dc.title.serialSensorsen
dc.identifier.doihttps://doi.org/10.3390/s21124186en
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen


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Creative Commons Attribution 4.0 International
License: Creative Commons Attribution 4.0 International