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Subspace Pursuit for Compressive Sensing Signal Reconstruction

Authors
  • Dai, Wei
  • Milenkovic, Olgica
Type
Preprint
Publication Date
Jan 08, 2009
Submission Date
Mar 06, 2008
Source
arXiv
License
Yellow
External links

Abstract

We propose a new method for reconstruction of sparse signals with and without noisy perturbations, termed the subspace pursuit algorithm. The algorithm has two important characteristics: low computational complexity, comparable to that of orthogonal matching pursuit techniques when applied to very sparse signals, and reconstruction accuracy of the same order as that of LP optimization methods. The presented analysis shows that in the noiseless setting, the proposed algorithm can exactly reconstruct arbitrary sparse signals provided that the sensing matrix satisfies the restricted isometry property with a constant parameter. In the noisy setting and in the case that the signal is not exactly sparse, it can be shown that the mean squared error of the reconstruction is upper bounded by constant multiples of the measurement and signal perturbation energies.

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