@InProceedings{7362435,
  author = {E. Ollila},
  booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
  title = {Nonparametric simultaneous sparse recovery: An application to source localization},
  year = {2015},
  pages = {509-513},
  abstract = {We consider multichannel sparse recovery problem where the objective is to find good recovery of jointly sparse unknown signal vectors from the given multiple measurement vectors which are different linear combinations of the same known elementary vectors. Many popular greedy or convex algorithms perform poorly under non-Gaussian heavy-tailed noise conditions or in the face of outliers. In this paper, we propose the usage of mixed ℓp, q norms on data fidelity (residual matrix) term and the conventional ℓ0,2-norm constraint on the signal matrix to promote row-sparsity. We devise a greedy pursuit algorithm based on simultaneous normalized iterative hard thresholding (SNIHT) algorithm. Simulation studies highlight the effectiveness of the proposed approaches to cope with different noise environments (i.i.d., row i.i.d, etc) and outliers. Usefulness of the methods are illustrated in source localization application with sensor arrays.},
  keywords = {compressed sensing;iterative methods;matrix algebra;nonparametric simultaneous multichannel sparse recovery problem;convex algorithms;greedy algorithms;nonGaussian heavy-tailed noise conditions;data fidelity term;signal matrix;simultaneous normalized iterative hard thresholding algorithm;SNIHT algorithm;source localization;compressed sensing;Yttrium;Signal processing algorithms;Robustness;Signal to noise ratio;Sparse matrices;Minimization;multichannel sparse recovery;compressed sensing;robustness;iterative hard thresholding},
  doi = {10.1109/EUSIPCO.2015.7362435},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570096183.pdf},
}

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In this paper, we propose the usage of mixed ℓp, q norms on data fidelity (residual matrix) term and the conventional ℓ0,2-norm constraint on the signal matrix to promote row-sparsity. We devise a greedy pursuit algorithm based on simultaneous normalized iterative hard thresholding (SNIHT) algorithm. Simulation studies highlight the effectiveness of the proposed approaches to cope with different noise environments (i.i.d., row i.i.d, etc) and outliers. 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Ollila},\n booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},\n title = {Nonparametric simultaneous sparse recovery: An application to source localization},\n year = {2015},\n pages = {509-513},\n abstract = {We consider multichannel sparse recovery problem where the objective is to find good recovery of jointly sparse unknown signal vectors from the given multiple measurement vectors which are different linear combinations of the same known elementary vectors. Many popular greedy or convex algorithms perform poorly under non-Gaussian heavy-tailed noise conditions or in the face of outliers. In this paper, we propose the usage of mixed ℓp, q norms on data fidelity (residual matrix) term and the conventional ℓ0,2-norm constraint on the signal matrix to promote row-sparsity. We devise a greedy pursuit algorithm based on simultaneous normalized iterative hard thresholding (SNIHT) algorithm. Simulation studies highlight the effectiveness of the proposed approaches to cope with different noise environments (i.i.d., row i.i.d, etc) and outliers. 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