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

This thesis is based on nine papers, all concerned with parameter estimation. The thesis aims at solving problems related to real-world applications such as spectroscopy, DNA sequencing, and audio processing, using sparse modeling heuristics. For the problems considered in this thesis, one is not only concerned with finding the parameters in the signal model, but also to determine the number of signal components present in the measurements. In recent years, developments in sparse modeling have allowed for methods that jointly estimate the parameters in the model and the model order. Based on these achievements, the approach often taken in this thesis is as follows. First, a parametric model of the considered signal is derived, containing ... toggle 12 keywords

parameter estimation – sparse models – convex optimization – symbolic periodicity – n-dimensional exponentially decaying sinusoids – alternating direction method of multipliers – covariance fitting – multi-pitch estimation – off-grid estimation – dictionary learning – atomic norm – sampling schemes.

Follow @eurasip