Ten lectures on wavelets
Field Computation by Moment Methods
Field Computation by Moment Methods
JPEG Still Image Data Compression Standard
JPEG Still Image Data Compression Standard
Extensions of compressed sensing
Signal Processing - Sparse approximations in signal and image processing
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Signal Reconstruction From Noisy Random Projections
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
A new, fast, and efficient image codec based on set partitioning in hierarchical trees
IEEE Transactions on Circuits and Systems for Video Technology
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Compressive sensing (CS) is a framework in which one attempts to measure a signal in a compressive mode, implying that fewer total measurements are required vis a vis direct sampling methods. Compressive sensing exploits the fact that the signal of interest is compressible in some basis, and the CS measurements correspond to projections (typically random projections) performed on the basis function coefficients. In this paper, we demonstrate that ideas from compressive sensing may be exploited in the context of electromagnetic modeling, here multi-static scattering from an arbitrary target. In this context, the computational analysis may be viewed as a numerical experiment, and ideas from compressive sensing may be used to reduce the number of computations required for target characterization. It is demonstrated that the compressive sensing framework may be applied with relatively minor modifications to many existing numerical models, with examples presented here for a fast-multipole computational engine.