Sparse Array Quiescent Beamformer Design Combining Adaptive and Deterministic Constraints
In this paper, we examine sparse array quiescent beamforming for multiple sources in interference-free environment. To maximize the output signal-to-noise ratio (SNR), the beamformer design comprises two intertwined stages, the determination of beamforming weights and the reconfiguration of array st...
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| Vydáno v: | IEEE transactions on antennas and propagation Ročník 65; číslo 11; s. 5808 - 5818 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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01.11.2017
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| ISSN: | 0018-926X, 1558-2221 |
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| Abstract | In this paper, we examine sparse array quiescent beamforming for multiple sources in interference-free environment. To maximize the output signal-to-noise ratio (SNR), the beamformer design comprises two intertwined stages, the determination of beamforming weights and the reconfiguration of array structure. The SNR maximization may produce high sidelobe levels, making the receiver vulnerable to interferences. We consider the problem of achieving maximum SNR beamforming subject to specified quiescent pattern constraints and, as such, combine both adaptive and deterministic approaches for sparse array configurations. We employ two convex relaxation methods and an iterative linear fractional programming algorithm to solve the nonconvex antenna selection problem for sparse array beamformers. Simulation examples demonstrate that the array configuration plays a vital role in determining the beamforming performance in interference-free scenarios. |
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| AbstractList | In this paper, we examine sparse array quiescent beamforming for multiple sources in interference-free environment. To maximize the output signal-to-noise ratio (SNR), the beamformer design comprises two intertwined stages, the determination of beamforming weights and the reconfiguration of array structure. The SNR maximization may produce high sidelobe levels, making the receiver vulnerable to interferences. We consider the problem of achieving maximum SNR beamforming subject to specified quiescent pattern constraints and, as such, combine both adaptive and deterministic approaches for sparse array configurations. We employ two convex relaxation methods and an iterative linear fractional programming algorithm to solve the nonconvex antenna selection problem for sparse array beamformers. Simulation examples demonstrate that the array configuration plays a vital role in determining the beamforming performance in interference-free scenarios. |
| Author | Xiangrong Wang Xianghua Wang Xianbin Cao Amin, Moeness |
| Author_xml | – sequence: 1 surname: Xiangrong Wang fullname: Xiangrong Wang email: xrwang@buaa.edu.cn organization: Sch. of Electron. & Inf. Eng., Beihang Univ., Beijing, China – sequence: 2 givenname: Moeness surname: Amin fullname: Amin, Moeness email: moeness.amin@villanova.edu organization: Center for Adv. Commun., Villanova Univ., Villanova, PA, USA – sequence: 3 surname: Xianghua Wang fullname: Xianghua Wang email: xianghuaw@pku.edu.cn organization: Coll. of Electr. Eng. & Autom., Shandong Univ. of Sci. & Technol., Qingdao, China – sequence: 4 surname: Xianbin Cao fullname: Xianbin Cao organization: Sch. of Electron. & Inf. Eng., Beihang Univ., Beijing, China |
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| CitedBy_id | crossref_primary_10_1016_j_sigpro_2020_107698 crossref_primary_10_1007_s00034_023_02597_8 crossref_primary_10_1016_j_dsp_2020_102780 crossref_primary_10_1155_2019_7130106 crossref_primary_10_1016_j_dsp_2020_102685 crossref_primary_10_1109_TSP_2021_3064183 crossref_primary_10_1080_02726343_2020_1750640 crossref_primary_10_1109_JSEN_2020_2977686 crossref_primary_10_1155_2019_1730868 crossref_primary_10_1109_TVT_2021_3133596 crossref_primary_10_1109_LSP_2020_2997253 crossref_primary_10_3390_rs17030477 crossref_primary_10_1016_j_sigpro_2023_109227 crossref_primary_10_1109_TAES_2018_2867258 crossref_primary_10_1109_JSEN_2023_3330936 crossref_primary_10_1109_TAES_2022_3207706 crossref_primary_10_3390_mi13060859 crossref_primary_10_1080_21681724_2023_2251766 crossref_primary_10_1109_TAP_2020_3008652 crossref_primary_10_1109_TSP_2017_2760279 crossref_primary_10_1109_TSP_2019_2911261 crossref_primary_10_1109_LCOMM_2022_3216865 crossref_primary_10_1016_j_dsp_2020_102678 crossref_primary_10_1109_TAES_2020_3037409 crossref_primary_10_1109_TSP_2019_2952052 crossref_primary_10_1109_TAES_2023_3285201 crossref_primary_10_1109_TAP_2020_3028190 |
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| SubjectTerms | Adaptive arrays Array signal processing Correlation Frame approximation Interference quadratic fractional quiescent beamforming sequential convex programming (SCP) Signal to noise ratio |
| Title | Sparse Array Quiescent Beamformer Design Combining Adaptive and Deterministic Constraints |
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