Huang, Jiayi ; Zhang, Xuan ; Wang, Xiangrong ; Zoubir, Abdelhak M. (2023)
Transmit Sparse Array Beamformer Design for Dual-Function Radar Communication Systems.
IEEE International Radar Conference (RADAR2023). Sydney, Australia (06.-10.11.2023)
doi: 10.1109/RADAR54928.2023.10371099
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Sparse arrays could achieve curtailed mutual coupling and reduced hardware cost while preserving an intact large array aperture, which is conducive to the joint design of radar and communication. In this paper, transmit sparse array beam former design for dual-function radar communication (DFRC) systems is discussed. We propose an integrated model that takes both radar transmit beam-forming for power concentration and array sparsity into account. Meanwhile, communication is realized by embedding information into beampattern via amplitude modulation (AM) and phase modulation (PM). Different from our previous work, one common sparse array with different beam-formers is designed for the constellation of all communication symbols. To solve this problem, we decompose the non-convex optimization problem subject to multiple constraints into two subproblems within the ADMM framework, and the subproblems are tackled via another ADMM iteration and sequential convex relaxation, respectively. Simulation results demonstrate that the proposed sparse array can obtain a lower sidelobe level using fewer antennas compared to that of uniformly-spaced array. More importantly, the dual functions of radar and communication are concurrently achieved with good performance.
Typ des Eintrags: | Konferenzveröffentlichung |
---|---|
Erschienen: | 2023 |
Autor(en): | Huang, Jiayi ; Zhang, Xuan ; Wang, Xiangrong ; Zoubir, Abdelhak M. |
Art des Eintrags: | Bibliographie |
Titel: | Transmit Sparse Array Beamformer Design for Dual-Function Radar Communication Systems |
Sprache: | Englisch |
Publikationsjahr: | 28 Dezember 2023 |
Ort: | Piscataway, NY |
Verlag: | IEEE |
Buchtitel: | 2023 IEEE International Radar Conference (RADAR) |
Veranstaltungstitel: | IEEE International Radar Conference (RADAR2023) |
Veranstaltungsort: | Sydney, Australia |
Veranstaltungsdatum: | 06.-10.11.2023 |
DOI: | 10.1109/RADAR54928.2023.10371099 |
Kurzbeschreibung (Abstract): | Sparse arrays could achieve curtailed mutual coupling and reduced hardware cost while preserving an intact large array aperture, which is conducive to the joint design of radar and communication. In this paper, transmit sparse array beam former design for dual-function radar communication (DFRC) systems is discussed. We propose an integrated model that takes both radar transmit beam-forming for power concentration and array sparsity into account. Meanwhile, communication is realized by embedding information into beampattern via amplitude modulation (AM) and phase modulation (PM). Different from our previous work, one common sparse array with different beam-formers is designed for the constellation of all communication symbols. To solve this problem, we decompose the non-convex optimization problem subject to multiple constraints into two subproblems within the ADMM framework, and the subproblems are tackled via another ADMM iteration and sequential convex relaxation, respectively. Simulation results demonstrate that the proposed sparse array can obtain a lower sidelobe level using fewer antennas compared to that of uniformly-spaced array. More importantly, the dual functions of radar and communication are concurrently achieved with good performance. |
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 > Signalverarbeitung |
Hinterlegungsdatum: | 15 Apr 2024 08:01 |
Letzte Änderung: | 18 Sep 2024 12:47 |
PPN: | 521578728 |
Export: | |
Suche nach Titel in: | TUfind oder in Google |
Frage zum Eintrag |
Optionen (nur für Redakteure)
Redaktionelle Details anzeigen |