Institutional Repository of Radio Astronomy Research Laboratory
| Compressed Sensing Based RFI Mitigation and Restoration for Pulsar Signals | |
Shan, Hao1,2 ; Yuan, Jianping1,2; Wang, Na1,2; Wang, Zhen1,2
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| 2022-08-01 | |
| Source Publication | Astrophysical Journal
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| ISSN | 0004-637X |
| Volume | 935Issue:2Pages:117 |
| Contribution Rank | 1 |
| Abstract | In pulsar signal processing, two primary difficulties are (1) radio-frequency interference (RFI) mitigation and (2) information loss due to preprocessing and mitigation itself. Linear mitigation methods have a difficulty in RFI modeling, and accommodate a limited range of RFI morphologies. Thresholding methods suffer from manual factors and adaptability. There is also a distinct lack of methods dedicated to information loss. In this paper, a novel method "CS-Pulsar" is proposed. It carries out compressed sensing (CS) on time-frequency signals to accomplish RFI mitigation and signal restoration simultaneously. Curvelets allow an optimal sparse representation for multichannel pulsar signals containing the time-of-arrival dispersion relationship. CS-Pulsar mitigation is implemented using a regularized least-squares framework that does not require the statistics of RFI to be known beforehand. CS-Pulsar implements channel restoration, and useful signal contents are retrieved from the measurement error by a morphological component analysis aided by the root-mean-square envelope. These two steps allow CS-Pulsar to provide key signal details for special astrophysical purposes. Experiments of signal restoration for pulsar data from the Nanshan 26 m radio telescope reveal the advantage of CS-Pulsar. The method successfully removes false peaks due to on-pulse RFI in multipeaked pulsar profiles. CS-Pulsar also increases the timing accuracy and signal-to-noise ratio proving its feasibilities and prospects in astrophysical measurements. |
| Keyword | frequency interference mitigation monotone-operators limits representations reconstruction decomposition convergence shrinkage recovery Astronomy & Astrophysics |
| Subtype | Article |
| DOI | 10.3847/1538-4357/ac8003 |
| Indexed By | SCI |
| Language | 英语 |
| WOS ID | WOS:000842708000001 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.xao.ac.cn/handle/45760611-7/5143 |
| Collection | 射电天文研究室_行星科学研究团组 科研仪器设备产出_利用南山26米射电望远镜（NSRT）观测数据的文章 |
| Affiliation | 1.Xinjiang Astronomical Observatory, Chinese Academy of Sciences, 150 Science Street 1st, Urumqi 830011, People’s Republic of China; 2.Xinjiang Key Laboratory of Radio Astrophysics, 150 Science Street 1st, Urumqi 830011, People’s Republic of China |
| First Author Affilication | Xinjiang Astronomical Observatory, Chinese Academy of Sciences |
| Recommended Citation GB/T 7714 | Shan, Hao,Yuan, Jianping,Wang, Na,et al. Compressed Sensing Based RFI Mitigation and Restoration for Pulsar Signals[J]. Astrophysical Journal,2022,935(2):117. |
| APA | Shan, Hao,Yuan, Jianping,Wang, Na,&Wang, Zhen.(2022).Compressed Sensing Based RFI Mitigation and Restoration for Pulsar Signals.Astrophysical Journal,935(2),117. |
| MLA | Shan, Hao,et al."Compressed Sensing Based RFI Mitigation and Restoration for Pulsar Signals".Astrophysical Journal 935.2(2022):117. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Shan-2022-Compressed（1047KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text | |
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