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2010•Journal of Dongguan University of TechnologyRequires access

Compressed Sensing Theory and Its Reconstruction Algorithm

YE Zhi-shen

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Abstract

The compressed sensing brings about a revolutionary breakthrough.It maintains the original signal structure by non-adaptive linear projection and samples the signal at much lower sampling rates than the Nyquist sampling rates.The signal can be exactly reconstructed by optimization.We analysyed the basic theory of compressed sensing and its two signal reconstruction algorithms including orthogonal matching pursuit and complementary orthogonal matching pursuit,and introduced the main application areas of the compressed sensing.

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What this paper is about

The compressed sensing brings about a revolutionary breakthrough.It maintains the original signal structure by non-adaptive linear projection and samples the signal at much lower sampling rates than the Nyquist sampling rates.The signal can be exactly reconstructed by optimization.We analysyed the basic theory of compressed sensing and its two signal reconstruction algorithms including orthogonal matching pursuit and complementary orthogonal matching pursuit,and introduced the main application areas of the compressed sensing.

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Available abstract

The compressed sensing brings about a revolutionary breakthrough.It maintains the original signal structure by non-adaptive linear projection and samples the signal at much lower sampling rates than the Nyquist sampling rates.The signal can be exactly reconstructed by optimization.We analysyed the basic theory of compressed sensing and its two signal reconstruction algorithms including orthogonal matching pursuit and complementary orthogonal matching pursuit,and introduced the main application areas of the compressed sensing.

Key concepts: Compressed sensing, Matching pursuit, Nyquist–Shannon sampling theorem, Signal reconstruction, SIGNAL (programming language), Algorithm, Sampling (signal processing), Nyquist rate

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