Rethinking Compressive Sensing. Campobello, G. In *2018 26th European Signal Processing Conference (EUSIPCO)*, pages 1765-1769, Sep., 2018.

Paper doi abstract bibtex

Paper doi abstract bibtex

In this paper we show that Compressive Sensing (CS) can be casted as an impulse response estimation problem. Using this interpretation we re-obtain some theoretical results of CS in a simple manner. Moreover, we prove that in the case of a randomly generated sensing matrix, reconstruction probability depends on the kurtosis of the distribution used for its generation.

@InProceedings{8553268, author = {G. Campobello}, booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)}, title = {Rethinking Compressive Sensing}, year = {2018}, pages = {1765-1769}, abstract = {In this paper we show that Compressive Sensing (CS) can be casted as an impulse response estimation problem. Using this interpretation we re-obtain some theoretical results of CS in a simple manner. Moreover, we prove that in the case of a randomly generated sensing matrix, reconstruction probability depends on the kurtosis of the distribution used for its generation.}, keywords = {compressed sensing;matrix algebra;probability;transient response;CS;simple manner;randomly generated sensing matrix;impulse response estimation problem;compressive sensing;Signal processing;Reconstruction algorithms;Linear systems;Estimation;Europe;Compressed sensing;Sparse matrices}, doi = {10.23919/EUSIPCO.2018.8553268}, issn = {2076-1465}, month = {Sep.}, url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570438975.pdf}, }

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