Abstract / truncated to 115 words (read the full abstract)

Sparsity-based estimation techniques deal with the problem of retrieving a data vector from an undercomplete set of linear observations, when the data vector is known to have few nonzero elements with unknown positions. It is also known as the atomic decomposition problem, and has been carefully studied in the field of compressed sensing. Recent findings have led to a method called basis pursuit, also known as Least Absolute Shrinkage and Selection Operator (LASSO), as a numerically reliable sparsity-based approach. Although the atomic decomposition problem is generally NP-hard, it has been shown that basis pursuit may provide exact solutions under certain assumptions. This has led to an extensive study of signals with sparse representation in different ... toggle 6 keywords

sparsity based techniques parameter estimation compressed sensing off-grid effect continuous basis pursuit sparsity based tracking


Panahi, Ashkan
Chalmers University of Technology
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May 5, 2015

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