Knowledge-Aided Normalized Iterative Hard Thresholding Algorithms for Sparse Recovery

This paper deals with the problem of sparse recovery often found in compressive sensing applications exploiting a priori knowledge. In particular, we present a knowledge-aided normalized iterative hard thresholding (KA-NIHT) algorithm that exploits information about the probabilities of nonzero entr...

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Veröffentlicht in:2018 26th European Signal Processing Conference (EUSIPCO) S. 1965 - 1969
Hauptverfasser: Jiang, Qianru, de Lamare, Rodrigo C., Zakharov, Yuriy, Li, Sheng, He, Xiongxiong
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: EURASIP 01.09.2018
Schlagworte:
Compressed sensing
Europe
Iterative algorithms
iterative hard thresholding
Matching pursuit algorithms
prior information
probability estimation
Signal processing
Signal processing algorithms
Simulation
sparse recovery
ISSN:2076-1465
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Abstract This paper deals with the problem of sparse recovery often found in compressive sensing applications exploiting a priori knowledge. In particular, we present a knowledge-aided normalized iterative hard thresholding (KA-NIHT) algorithm that exploits information about the probabilities of nonzero entries. We also develop a strategy to update the probabilities using a recursive KA-NIHT (RKA-NIHT) algorithm, which results in improved recovery. Simulation results illustrate and compare the performance of the proposed and existing algorithms.
AbstractList This paper deals with the problem of sparse recovery often found in compressive sensing applications exploiting a priori knowledge. In particular, we present a knowledge-aided normalized iterative hard thresholding (KA-NIHT) algorithm that exploits information about the probabilities of nonzero entries. We also develop a strategy to update the probabilities using a recursive KA-NIHT (RKA-NIHT) algorithm, which results in improved recovery. Simulation results illustrate and compare the performance of the proposed and existing algorithms.
Author He, Xiongxiong
de Lamare, Rodrigo C.
Jiang, Qianru
Li, Sheng
Zakharov, Yuriy
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  givenname: Rodrigo C.
  surname: de Lamare
  fullname: de Lamare, Rodrigo C.
  organization: Department of Electronic Engineering, University of York, York, YO10 5DD, U.K
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  givenname: Yuriy
  surname: Zakharov
  fullname: Zakharov, Yuriy
  organization: Department of Electronic Engineering, University of York, York, YO10 5DD, U.K
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  givenname: Sheng
  surname: Li
  fullname: Li, Sheng
  organization: College of Information Engineering, Zhejiang University of Technology, Zhejiang, People's Republic of China
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  givenname: Xiongxiong
  surname: He
  fullname: He, Xiongxiong
  organization: College of Information Engineering, Zhejiang University of Technology, Zhejiang, People's Republic of China
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Snippet This paper deals with the problem of sparse recovery often found in compressive sensing applications exploiting a priori knowledge. In particular, we present a...
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StartPage 1965
SubjectTerms Compressed sensing
Europe
Iterative algorithms
iterative hard thresholding
Matching pursuit algorithms
prior information
probability estimation
Signal processing
Signal processing algorithms
Simulation
sparse recovery
Title Knowledge-Aided Normalized Iterative Hard Thresholding Algorithms for Sparse Recovery
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