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Robust Dynamic Trajectory Optimization for UAV-Aided Localization of Ground Target

Xiang, Lin ; Zhang, Mengshuai ; Klein, Anja (2023)
Robust Dynamic Trajectory Optimization for UAV-Aided Localization of Ground Target.
2023 IEEE Global Communications Conference. Kuala Lumpur, Malaysia (04.-08.12.2023)
doi: 10.1109/GLOBECOM54140.2023.10437337
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

In this paper, we consider employing an unmanned aerial vehicle (UAV) equipped with an onboard radar transceiver to localize a ground target at an unknown position. Exploiting the UAV’s mobility, we aim to gather line-of-sight (LoS) range measurements from favorable waypoints and improve the ensuing multi-lateration process while estimating the target’s location. To this end, we introduce a novel localization error metric, characterized geometrically by the radius of a defined confidence region where the target resides at a predetermined confidence level. Additionally, we investigate robust dynamic optimization of the UAV’s trajectory to minimize the defined localization error metric online, utilizing sequentially available but delayed range estimates. The formulated optimization problem belongs to a convex-nonconcave minimax problem, which is generally intractable. To solve this problem, we further propose two iterative online algorithms based on semidefinite programming (SDP) relaxation and alternating/sequential convex optimization techniques. Simulation results show that the proposed online schemes outperform several benchmarks, either in the final localization accuracy or in the rate of decreasing the localization error.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2023
Autor(en): Xiang, Lin ; Zhang, Mengshuai ; Klein, Anja
Art des Eintrags: Bibliographie
Titel: Robust Dynamic Trajectory Optimization for UAV-Aided Localization of Ground Target
Sprache: Englisch
Publikationsjahr: 4 Dezember 2023
Veranstaltungstitel: 2023 IEEE Global Communications Conference
Veranstaltungsort: Kuala Lumpur, Malaysia
Veranstaltungsdatum: 04.-08.12.2023
DOI: 10.1109/GLOBECOM54140.2023.10437337
URL / URN: https://ieeexplore.ieee.org/document/10437337
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Kurzbeschreibung (Abstract):

In this paper, we consider employing an unmanned aerial vehicle (UAV) equipped with an onboard radar transceiver to localize a ground target at an unknown position. Exploiting the UAV’s mobility, we aim to gather line-of-sight (LoS) range measurements from favorable waypoints and improve the ensuing multi-lateration process while estimating the target’s location. To this end, we introduce a novel localization error metric, characterized geometrically by the radius of a defined confidence region where the target resides at a predetermined confidence level. Additionally, we investigate robust dynamic optimization of the UAV’s trajectory to minimize the defined localization error metric online, utilizing sequentially available but delayed range estimates. The formulated optimization problem belongs to a convex-nonconcave minimax problem, which is generally intractable. To solve this problem, we further propose two iterative online algorithms based on semidefinite programming (SDP) relaxation and alternating/sequential convex optimization techniques. Simulation results show that the proposed online schemes outperform several benchmarks, either in the final localization accuracy or in the rate of decreasing the localization error.

Freie Schlagworte: Open6GHUB, DAAD, emergenCITY, emergenCITY_KOM
Zusätzliche Informationen:

Signal Processing for Communications

Fachbereich(e)/-gebiet(e): 18 Fachbereich Elektrotechnik und Informationstechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Kommunikationstechnik
LOEWE
LOEWE > LOEWE-Zentren
LOEWE > LOEWE-Zentren > emergenCITY
Hinterlegungsdatum: 25 Jan 2024 10:43
Letzte Änderung: 25 Apr 2024 08:06
PPN: 517481111
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