<div class="csl-bib-body">
<div class="csl-entry">d`Àpolito, F., Eliasch, C., Sulzbachner, C., & Mecklenbrauker, C. (2022). A Joint Multiple Hypothesis Tracking and Particle Filter Approach for Aerial Data Fusion. In <i>2022 25th International Conference on Information Fusion (FUSION)</i> (pp. 1–7). IEEE. https://doi.org/10.23919/FUSION49751.2022.9841308</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/80336
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dc.description.abstract
The use of Unmanned Aerial Vehicles (UAV) has increased in recent years. Increased density of air traffic as well as the autonomy of the vehicles involved, demand robust safety of traffic operations in terms of dependable decision making for flight operations. Since future traffic management services (U-space) will focus on registration, identification, approval to fly, etc., and cooperative traffic avoidance such as FLARM requires that other parties be equipped as well, future UAVs should be able to robustly detect uncooperative parties and avoid mid-air collisions in airspace. To ensure the highest robustness and to increase sensitivity and accuracy, a combination of several sensors systems by multi-sensor data fusion techniques is highly recommended. This paper formulates a novel multi sensor data fusion algorithm, that is a joint approach of Multiple Hypothesis Tracking algorithm and Particle Filtering. The union of these two algorithms combines the strength of the Multiple Hypothesis Tracking for data association with the robustness of the Particle Filter to estimate the position of the tracked objects. This joint approach has been validated with the use of simulated data.
en
dc.description.sponsorship
FFG - Österr. Forschungsförderungs- gesellschaft mbH
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dc.language.iso
en
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dc.publisher
IEEE
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dc.subject
sensor fusion
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dc.subject
Unmanned Aerial Vehicles (UAV)
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dc.title
A Joint Multiple Hypothesis Tracking and Particle Filter Approach for Aerial Data Fusion
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dc.type
Inproceedings
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dc.type
Konferenzbeitrag
de
dc.contributor.affiliation
Austrian Institute of Technology
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dc.contributor.affiliation
Austrian Insitute of Technology
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dc.relation.isbn
978-1-7377497-2-1
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dc.relation.doi
10.23919/FUSION49751.2022
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dc.description.startpage
1
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dc.description.endpage
7
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dc.relation.grantno
867400
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dcterms.dateSubmitted
2022-03-15
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
2022 25th International Conference on Information Fusion (FUSION)
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tuw.project.title
Datenfusion von K/X-Band Radar mit visuellen Sensoren für luftgestützte Plattformen