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A Vehicle-Mounted Radar-Vision System for Precisely Positioning Clustering UAVs

Wu, G; Zhou, F; Wong, KK; Li, XY; (2024) A Vehicle-Mounted Radar-Vision System for Precisely Positioning Clustering UAVs. IEEE Journal on Selected Areas in Communications 10.1109/JSAC.2024.3414610. (In press). Green open access

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Abstract

The clustering unmanned aerial vehicles (UAVs) positioning is significant for preventing unauthorized clustering UAVs from causing physical and informational damages. However, current positioning systems suffer from limited sensing view and positioning range, which result in poor positioning performance. In order to tackle those issues, a novel vehicle-mounted radar-vision clustering UAVs positioning system is developed, which achieves precise, wide-area, and dynamic-view sensing and positioning of the clustering UAVs. Moreover, a matching-based spatiotemporal fusion framework is established to mitigate cross-modal and cross-view spatiotemporal misalignment by adaptively exploiting the cross-modal and cross-view feature correlations. Furthermore, we propose an attention-based spatiotemporal fusion method that achieves a trinity projective attention with the unique structure and task-oriented format for effective feature matching and precise clustering UAVs positioning. Our method also exploited the modality-oriented cross-modal feature and the UAV-motion-oriented cross-view UAV spatiotemporal motion feature.We demonstrate the advantages of our proposed framework and positioning method in our developed clustering UAVs positioning system in practice. Experimental results confirm that our proposed method outperforms the benchmark methods in terms of the positioning precision, especially under the occlusion scenarios. Moreover, ablation studies confirm the effectiveness of each unit of our method.

Type: Article
Title: A Vehicle-Mounted Radar-Vision System for Precisely Positioning Clustering UAVs
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/JSAC.2024.3414610
Publisher version: http://dx.doi.org/10.1109/jsac.2024.3414610
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Precise clustering UAVs positioning, radar-vision cross-modal, mobile spatiotemporal fusion, projective attention
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10194131
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