High SNR consistent compressive sensing

High SNR consistent compressive sensing

High signal to noise ratio (SNR) consistency of model selection criteria in linear regression models has attracted a lot of attention recently. However, most of the existing literature on high SNR consistency deals with model order selection. Further, the limited literature available on the high SNR consistency of subset selection procedures (SSPs) is applicable to linear regression with full r...

Compressive sensing

Michael B. Wakin is the Ben L. Fryrear Associate Professor in the Department of Electrical Engineering and Computer Science at the Colorado School of Mines (CSM). Dr. Wakin received a B.S. in electrical engineering and a B.A. in mathematics in 2000 (summa cum laude), an M.S. in electrical engineering in 2002, and a Ph.D. in electrical engineering in 2007, all from Rice University. He was an NSF...

Compressive Sensing

Compressive sensing is a new type of sampling theory, which predicts that sparse signals and images can be reconstructed from what was previously believed to be incomplete information. As a main feature, efficient algorithms such as l1-minimization can be used for recovery. The theory has many potential applications in signal processing and imaging. This chapter gives an introduction and overvi...

Compressive Sensing

Initial Analysis of SNR / Sampling Rate Constraints in Compressive Sensing based Imaging Radar

The compressive sensing based imaging radar, as a combination of compressive sensing (CS) and microwave imaging, is attracting people’s interest for its several advantages, but also facing application difficulties including the lack of performance evaluating tools. People have developed some tools e.g. RIP, MC and phase transit to analyze and evaluate the performance of a sparse reconstruction ...