中国邮电高校学报(英文) ›› 2018, Vol. 25 ›› Issue (1): 62-69.

• Artificial Intelligence • 上一篇    下一篇

One-bit compressed sensing recovery algorithm robust to perturbation

崔宇鹏1,徐文波1,林家儒   

  • 收稿日期:2017-07-17 修回日期:2018-01-18 出版日期:2018-02-28 发布日期:2018-02-28
  • 通讯作者: 崔宇鹏 E-mail:cypbupt@bupt.edu.cn
  • 基金资助:
    国家自然科学基金

One-bit compressed sensing recovery algorithm robust to perturbation

  • Received:2017-07-17 Revised:2018-01-18 Online:2018-02-28 Published:2018-02-28
  • Contact: Yu-Peng CUI E-mail:cypbupt@bupt.edu.cn
  • Supported by:
    Natural Science Foundation of China

摘要: One-bit compressed sensing(CS) technology reconstructs the sparse signal when the available measurements are reduced to only their sign-bit. It is well known that CS reconstruction should know the measurement matrix exactly to obtain a correct result. However, the measurement matrix is probably perturbed in many practical scenarios. An iterative algorithm called perturbed binary iterative hard thresholding (PBIHT) is proposed to reconstruct the sparse signal from the binary measurements (sign measurements) where the measurement matrix experiences a general perturbation. The proposed algorithm can reconstruct the original data without any prior knowledge about the perturbation. Specifically, using the ideas of the gradient descent, PBIHT iteratively estimates signal and perturbation until the estimation converges. Simulation results demonstrate that, under certain conditions, PBIHT improves the performance of signal reconstruction in the perturbation scenario.

关键词: one-bit,compressed sensing,reconstruction algorithm,perturbation,robust

Abstract: One-bit compressed sensing(CS) technology reconstructs the sparse signal when the available measurements are reduced to only their sign-bit. It is well known that CS reconstruction should know the measurement matrix exactly to obtain a correct result. However, the measurement matrix is probably perturbed in many practical scenarios. An iterative algorithm called perturbed binary iterative hard thresholding (PBIHT) is proposed to reconstruct the sparse signal from the binary measurements (sign measurements) where the measurement matrix experiences a general perturbation. The proposed algorithm can reconstruct the original data without any prior knowledge about the perturbation. Specifically, using the ideas of the gradient descent, PBIHT iteratively estimates signal and perturbation until the estimation converges. Simulation results demonstrate that, under certain conditions, PBIHT improves the performance of signal reconstruction in the perturbation scenario.

Key words: one-bit,compressed sensing,reconstruction algorithm,perturbation,robust

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