The matching pursuit algorithm derives an expansion of a signal in terms of the elements of a large dictionary of time-frequency atoms. This paper considers the use of matching pursuit for computing signal expansions in terms of damped sinusoids. First, expansion based on complex damped sinusoids is explored; it is shown that the expansion can be e ciently derived using the FFT and simple recur...

This article demonstrates theoretically and empirically that a greedy algorithm called Orthogonal Matching Pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension d given O(m ln d) random linear measurements of that signal. This is a massive improvement over previous results for OMP, which require O(m) measurements. The new results for OMP are comparable with recent resu...

In this paper, we consider the problem of compressed sensing where the goal is to recover all sparsevectors using a small number of fixed linear measurements. For this problem, we propose a novelpartial hard-thresholding operator that leads to a general family of iterative algorithms. While oneextreme of the family yields well known hard thresholding algorithms like ITI and HTP[17, ...