Small-scale location identification in natural environments with deep learning based on biomimetic sonar echoes

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dc.contributor.authorZhang, Liujunen
dc.contributor.authorFarabow, Andrewen
dc.contributor.authorSinghal, Pradyumannen
dc.contributor.authorMueller, Rolfen
dc.date.accessioned2023-02-13T17:29:24Zen
dc.date.available2023-02-13T17:29:24Zen
dc.date.issued2023-01-20en
dc.date.updated2023-02-13T01:02:40Zen
dc.description.abstractMany bat species navigate in complex, heavily vegetated habitats. To achieve this, the animal relies on a sensory basis that is very different from what is typically done in engineered systems that are designed for outdoor navigation. Whereas the engineered systems rely on data-heavy senses such as lidar, bats make do with echoes triggered by short, ultrasonic pulses. Prior work has shown that "clutter echoes" originating from vegetation can convey information on the environment they were recorded in -- despite their unpredictable nature. The current work has investigated the spatial granularity that these clutter echoes can convey by applying deep-learning location identification to an echo data set that resulted from the dense spatial sampling of a forest environment. The GPS location corresponding to the echo collection events was clustered to break the survey area into the number of spatial patches ranging from two to 100. A convolutional neural network (Resnet 152) was used to identify the patch associated with echo sets ranging from one to ten echoes. The results demonstrate a spatial resolution that is comparable to the accuracy of recreation-grade GPS operating under foliage cover. This demonstrates that fine-grained location identification can be accomplished at very low data rates.en
dc.description.versionAccepted versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1088/1748-3190/acb51fen
dc.identifier.eissn1748-3190en
dc.identifier.issn1748-3182en
dc.identifier.orcidMueller, Rolf [0000-0001-8358-4053]en
dc.identifier.pmid36669200en
dc.identifier.urihttp://hdl.handle.net/10919/113820en
dc.language.isoenen
dc.publisherIOPen
dc.relation.urihttps://www.ncbi.nlm.nih.gov/pubmed/36669200en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectBiomimetic roboten
dc.subjectBiosonar navigationen
dc.subjectMachine learningen
dc.subjectNavigation granularity in foresten
dc.subjectNeurosciencesen
dc.subjectBioengineeringen
dc.titleSmall-scale location identification in natural environments with deep learning based on biomimetic sonar echoesen
dc.title.serialBioinspiration & Biomimeticsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherJournal Articleen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Mechanical Engineeringen
pubs.organisational-group/Virginia Tech/Faculty of Health Sciencesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen

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