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.

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}, }

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