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Article Navigation> JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY> 2018> 27(2): 161-168
Jiangmei Zhang, Haibo Ji, Qingping Zhu, Hongsen He, Kunpeng Wang. Pulse Signal Recovery Method Based on Sparse Representation[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2018, 27(2): 161-168. doi: 10.15918/j.jbit1004-0579.201827.0201
Citation: Jiangmei Zhang, Haibo Ji, Qingping Zhu, Hongsen He, Kunpeng Wang. Pulse Signal Recovery Method Based on Sparse Representation[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2018, 27(2): 161-168.doi:10.15918/j.jbit1004-0579.201827.0201
shu

Pulse Signal Recovery Method Based on Sparse Representation

doi:10.15918/j.jbit1004-0579.201827.0201
  • 1.

    Department of Automation, University of Science and Technology of China, Hefei 230027, China;School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, Sichuan, China

  • 2.

    Department of Automation, University of Science and Technology of China, Hefei 230027, China

  • 3.

    School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, Sichuan, China

  • Received Date:2017-04-13
    Abstract
  • Pulse signal recovery is to extract useful amplitude and time information from the pulse signal contaminated by noise. It is a great challenge to precisely recover the pulse signal in loud background noise. The conventional approaches, which are mostly based on the distribution of the pulse energy spectrum, do not well determine the locations and shapes of the pulses. In this paper, we propose a time domain method to reconstruct pulse signals. In the proposed approach, a sparse representation model is established to deal with the issue of the pulse signal recovery under noise conditions. The corresponding problem based on the sparse optimization model is solved by a matching pursuit algorithm. Simulations and experiments validate the effectiveness of the proposed approach on pulse signal recovery.
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    • References (16)
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