Knowledge-Aided Normalized Iterative Hard Thresholding Algorithms for Sparse Recovery. Jiang, Q., de Lamare , R. C., Zakharov, Y., Li, S., & He, X. In 2018 26th European Signal Processing Conference (EUSIPCO), pages 1965-1969, Sep., 2018.
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Paper doi abstract bibtex
Paper doi abstract bibtex 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.
@InProceedings{8553389,
author = {Q. Jiang and R. C. {de Lamare} and Y. Zakharov and S. Li and X. He},
booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},
title = {Knowledge-Aided Normalized Iterative Hard Thresholding Algorithms for Sparse Recovery},
year = {2018},
pages = {1965-1969},
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.},
keywords = {approximation theory;compressed sensing;iterative methods;probability;RKA-NIHT;knowledge-aided normalized iterative hard thresholding algorithms;sparse recovery;compressive sensing applications;recursive KA-NIHT algorithm;Signal processing algorithms;Matching pursuit algorithms;Europe;Signal processing;Compressed sensing;Iterative algorithms;Simulation;compressed sensing;iterative hard thresholding;prior information;probability estimation;sparse recovery},
doi = {10.23919/EUSIPCO.2018.8553389},
issn = {2076-1465},
month = {Sep.},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570437058.pdf},
}Downloads: 0
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