Journal of Information Processing Systems

  • 제15권2호
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  • Pages.410-421
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  • 2019
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  • 1976-913X(pISSN)
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  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
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Block Sparse Signals Recovery Algorithm for Distributed Compressed Sensing Reconstruction

  • Chen, Xingyi (Faculty of Information Engineering, China University of Geosciences) ;
  • Zhang, Yujie (School of Mathematics and Physics, China University of Geosciences) ;
  • Qi, Rui (School of Science, Naval University of Engineering)
  • 투고 : 2017.08.11
  • 심사 : 2018.07.14
  • 발행 : 2019.04.30
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⟨ 이전 논문 다음 논문 ⟩

초록

Distributed compressed sensing (DCS) states that we can recover the sparse signals from very few linear measurements. Various studies about DCS have been carried out recently. In many practical applications, there is no prior information except for standard sparsity on signals. The typical example is the sparse signals have block-sparse structures whose non-zero coefficients occurring in clusters, while the cluster pattern is usually unavailable as the prior information. To discuss this issue, a new algorithm, called backtracking-based adaptive orthogonal matching pursuit for block distributed compressed sensing (DCSBBAOMP), is proposed. In contrast to existing block methods which consider the single-channel signal reconstruction, the DCSBBAOMP resorts to the multi-channel signals reconstruction. Moreover, this algorithm is an iterative approach, which consists of forward selection and backward removal stages in each iteration. An advantage of this method is that perfect reconstruction performance can be achieved without prior information on the block-sparsity structure. Numerical experiments are provided to illustrate the desirable performance of the proposed method.

키워드

E1JBB0_2019_v15n2_410_f0002.png 이미지

Fig. 1. Reconstruction results over the sparse_value. The numerical values on x-axis denote the sparse_value of signals and those on y-axis represent the SNR (a) and run time (b).

E1JBB0_2019_v15n2_410_f0008.png 이미지

Fig. 7. The electrocardiography (ECG) signals in signal channel no#1 of three patients which are selected randomly from the PTB Diagnostic ECG Database: (a) the original signals , (b) with orthogonal Daubechies waveles (db1), (c) $\tilde{X}$ recovered by our algorithm, and (D) $\tilde{\theta}$ recovered by our algorithm.

E1JBB0_2019_v15n2_410_f0009.png 이미지

Fig. 3. Reconstruction results over the sparse_value. The numerical values on x-axis denote the sparse_ value and those on y-axis represent the SNR (a) and run time (b).

E1JBB0_2019_v15n2_410_f0010.png 이미지

Fig. 4. Reconstruction results over the number of block d. The numerical values on x-axis denote the number of block d and those on y-axis represent the SNR (a) and run time (b).

E1JBB0_2019_v15n2_410_f0011.png 이미지

Fig. 5. Reconstruction results with unknown block d. The numerical values on x-axis denote the number of block d and those on y-axis represent the SNR (a) and run time (b); when we generate the source with block size d = 8.

E1JBB0_2019_v15n2_410_f0012.png 이미지

Fig. 2. Reconstruction results over the number of measurement M. The numerical values on x-axis denote the number of measurement M and those on y-axis represent the SNR (a) and run time (b).

E1JBB0_2019_v15n2_410_f0013.png 이미지

Fig. 6. Reconstruction results with unknown block d. The numerical values on x-axis denote the number of block d and those on y-axis represent the SNR (a) and run time (b); when we generate the source with block size d = 5.

Table 1. Average reconstruction SNR and run time of block sparse signals using different methods

E1JBB0_2019_v15n2_410_t0001.png 이미지

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