Uniform Recovery Bounds for Structured Random Matrices in Corrupted Compressed Sensing - 2018 PROJECT TITLE :Uniform Recovery Bounds for Structured Random Matrices in Corrupted Compressed Sensing - 2018ABSTRACT:We study the problem of recovering an s-sparse signal x* ? C n from corrupted measurements y = Ax* + z* + w, where z* ? C m is a k-sparse corruption vector whose nonzero entries could be arbitrarily giant and w ? C mis a dense noise with bounded energy. The aim is to exactly and stably recover the sparse signal with tractable optimization programs. During this Project, we have a tendency to prove the uniform recovery guarantee of this drawback for two classes of structured sensing matrices. The primary class can be expressed as the merchandise of a unit-norm tight frame (UTF), a random diagonal matrix, and a bounded columnwise orthonormal matrix (e.g., partial random circulant matrix). When the UTF is bounded (i.e. µ(U) ~ 1/vm), we prove that with high probability, one can recover an s-sparse signal precisely and stably by l one minimization programs whether or not the measurements are corrupted by a sparse vector, provided m = O(s log 2 s log two n) and therefore the sparsity level k of the corruption could be a constant fraction of the total variety of measurements. The second category considers a randomly subsampled orthonormal matrix (e.g., random Fourier matrix). We have a tendency to prove the uniform recovery guarantee provided that the corruption is sparse on bound sparsifying domain. Numerous simulation results also are presented to verify and complement the theoretical results. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest System Architecture and Signal Processing for Frequency-Modulated Continuous-Wave Radar Using Active Backscatter Tags - 2018 Windowed State-Space Filters for Signal Detection and Separation - 2018