Compressive Beamforming Based on Multiconstraint Bayesian Framework
文献类型：期刊论文
| 作者 | Li, Chao2; Zhou, Tian2,3; Guo, Qijia2; Cui, Hong-Liang1,4
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| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
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| 出版日期 | 2021-11-01 |
| 卷号 | 59期号:11页码:9209-9223 |
| 关键词 | Bayesian sparse learning (BSL) deterministic compressive sensing (CS) multiconstraint multisnapshot beamforming multiple signal classification (MUSIC) |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2021.3056187 |
| 通讯作者 | Guo, Qijia(gqj@hrbeu.edu.cn) |
| 英文摘要 | Compressive sensing (CS) is a promising technique recognized for its merits in recovering sparse signals with enhanced resolution entailing specific constraints. In recasting the CS model within a Bayesian framework, its formulation can be interpreted and solved under various prior assumptions that may correspond to, e.g., the L 1 or reweighted L 1 constraint. Bayesian CS (or Bayesian sparse learning, BSL) achieves improved resolution and robustness compared with the deterministic CS. Therefore, BSL has been invoked to solve the single and multisnapshot beamforming models for direction-of-arrival (DOA) estimation. However, the recovery performance deteriorates for complicated signals because of nonsparsity. In this article, a multiconstraint BSL approach is proposed to solve the multisnapshot beamforming model (termed M-MCRBSL), which reconstructs the amplitude and DOA of the source simultaneously. With a proper assembly of constraints, the source can be represented sparsely and the transformation coefficients can be recovered accurately in multiple sparse domains. As demonstrated in the simulations, M-MCRBSL outperforms other state-of-the-art multisnapshot beamforming methods gauged by both the normalized mean square error (NMSE) and the structural similarity (SSIM) index. In particular, a deficiency sensitivity experiment is devised to elaborate the feasibility of the invoked invertibility approximation. As attested with an underwater acoustics experiment, M-MCRBSL with joint identity and Haar wavelet constraints achieves improved performance in terms of enhanced resolution and suppressed interference sources. |
| 资助项目 | National Natural Science Foundation of China[52001097] ; National Natural Science Foundation of China[U1709203] ; Hei Long Jiang Postdoctoral Foundation[LBH-Z20127] ; Fundamental Research Funds for the Central Universities[3072020CFJ0505] |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000711850900025 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/14550]  |
| 专题 | 中国科学院重庆绿色智能技术研究院 |
| 通讯作者 | Guo, Qijia |
| 作者单位 | 1.Jilin Univ, Coll Instrumentat & Elect Engn, Changchun 130012, Peoples R China 2.Harbin Engn Univ, Key Lab Marine Informat Acquisit & Secur, Minist Ind & Informat Technol, Acoust Sci & Technol Lab,Coll Underwater Acoust E, Harbin 150001, Peoples R China 3.Peng Cheng Lab, Shenzhen 518055, Peoples R China 4.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Chao,Zhou, Tian,Guo, Qijia,et al. Compressive Beamforming Based on Multiconstraint Bayesian Framework[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2021,59(11):9209-9223. |
| APA | Li, Chao,Zhou, Tian,Guo, Qijia,&Cui, Hong-Liang.(2021).Compressive Beamforming Based on Multiconstraint Bayesian Framework.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,59(11),9209-9223. |
| MLA | Li, Chao,et al."Compressive Beamforming Based on Multiconstraint Bayesian Framework".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 59.11(2021):9209-9223. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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