Kirchhoff Beam Migration Based on Compressive Sensing

Interferometric ISAR Imaging Based on Compressive Sensing

Inverse Synthetic Aperture Radar (ISAR) images are often used for target classification and recognition applications. However, conventional 2D images do not provide the height information about the scattering centers. In this paper, an interferometric ISAR imaging method based on compressive sensing (CS) is proposed that is able to estimate the scatterering centres heights. The interferometric ...

Bayesian Compressive Sensing Based on Importance Models

To solve the problem that all row signals use the same reconstruction algorithm, a type of Bayesian compressive sensing based on importance models is proposed, which reconstructs more important signals firstly even if losing some unimportant signals. Compared to Bayesian compressive sensing whose performances is not well when sampling ratio is lower, the proposed algorithms can improve reconstr...

Image Reconstruction based on Block-based Compressive Sensing

The data of interest are assumed to be represented as Ndimensional real vectors, and these vectors are compressible in some linear basis B, implying that the signals can be reconstructed accurately using only a small number of basis function coefficients associated with B. A new approach based on Compressive Sensing (CS) framework which is a theory that one may achieve an exact signal reconstru...

Introduction to Kirchhoff migration programs

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...