Radar Ghost Target Detection via Multimodal Transformers
Radar Ghost Target Detection via Multimodal Transformers
No Thumbnail Available
Files
There are no files associated with this item.
Date
2021
Authors
Editors
Journal ISSN
Electronic ISSN
ISBN
Bibliographical data
Publisher
Series
DOI (citable link)
International patent number
Link to the license
oops
EU project number
Project
Open Access publication
Collections
Title in another language
Publication type
Journal article
Publication status
Published
Published in
IEEE Robotics and Automation Letters ; 6 (2021), 4. - pp. 7758-7765. - IEEE. - ISSN 2377-3774. - eISSN 2377-3766
Abstract
Ghost targets caused by inter-reflections are by design unavoidable in radar measurements, and it is challenging to distinguish these artifact detections from real ones. In this letter, we propose a novel approach to detect radar ghost targets by using LiDAR data as a reference. For this, we adopt a multimodal transformer network to learn interactions between points. We employ self-attention to exchange information between radar points, and local crossmodal attention to infuse information from surrounding LiDAR points. The key idea is that a ghost target should have higher semantic affinity with the reflected real target than the other ones. Extensive experiments on nuScenes [1] show that our method outperforms the baseline method on radar ghost target detection by a large margin.
Summary in another language
Subject (DDC)
004 Computer Science
Keywords
Conference
Review
undefined / . - undefined, undefined. - (undefined; undefined)
Cite This
ISO 690
WANG, Leichen, Simon GIEBENHAIN, Carsten ANKLAM, Bastian GOLDLÜCKE, 2021. Radar Ghost Target Detection via Multimodal Transformers. In: IEEE Robotics and Automation Letters. IEEE. 6(4), pp. 7758-7765. ISSN 2377-3774. eISSN 2377-3766. Available under: doi: 10.1109/LRA.2021.3100176BibTex
@article{Wang2021Radar-54809, year={2021}, doi={10.1109/LRA.2021.3100176}, title={Radar Ghost Target Detection via Multimodal Transformers}, number={4}, volume={6}, issn={2377-3774}, journal={IEEE Robotics and Automation Letters}, pages={7758--7765}, author={Wang, Leichen and Giebenhain, Simon and Anklam, Carsten and Goldlücke, Bastian} }
RDF
<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:void="http://rdfs.org/ns/void#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/54809"> <dc:language>eng</dc:language> <dc:contributor>Giebenhain, Simon</dc:contributor> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dc:creator>Wang, Leichen</dc:creator> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-09-07T07:01:03Z</dcterms:available> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-09-07T07:01:03Z</dc:date> <foaf:homepage rdf:resource="http://localhost:8080/"/> <dcterms:abstract xml:lang="eng">Ghost targets caused by inter-reflections are by design unavoidable in radar measurements, and it is challenging to distinguish these artifact detections from real ones. In this letter, we propose a novel approach to detect radar ghost targets by using LiDAR data as a reference. For this, we adopt a multimodal transformer network to learn interactions between points. We employ self-attention to exchange information between radar points, and local crossmodal attention to infuse information from surrounding LiDAR points. The key idea is that a ghost target should have higher semantic affinity with the reflected real target than the other ones. Extensive experiments on nuScenes [1] show that our method outperforms the baseline method on radar ghost target detection by a large margin.</dcterms:abstract> <dc:creator>Goldlücke, Bastian</dc:creator> <dcterms:title>Radar Ghost Target Detection via Multimodal Transformers</dcterms:title> <dc:creator>Anklam, Carsten</dc:creator> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <dc:contributor>Goldlücke, Bastian</dc:contributor> <dcterms:issued>2021</dcterms:issued> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/54809"/> <dc:creator>Giebenhain, Simon</dc:creator> <dc:contributor>Wang, Leichen</dc:contributor> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <dc:contributor>Anklam, Carsten</dc:contributor> </rdf:Description> </rdf:RDF>
Internal note
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Examination date of dissertation
Method of financing
Comment on publication
Alliance license
Corresponding Authors der Uni Konstanz vorhanden
International Co-Authors
Bibliography of Konstanz
Yes
Refereed
Yes