Universal Measurement Matrix Design for Sparse and Co-Sparse Signal Recovery
نویسندگان
چکیده
منابع مشابه
Universal Measurement Bounds for Structured Sparse Signal Recovery
Standard compressive sensing results state that to exactly recover an s sparse signal in R, one requires O(s · log p) measurements. While this bound is extremely useful in practice, often real world signals are not only sparse, but also exhibit structure in the sparsity pattern. We focus on group-structured patterns in this paper. Under this model, groups of signal coefficients are active (or i...
متن کاملAnti-measurement Matrix Uncertainty Sparse Signal Recovery for Compressive Sensing
Compressive sensing (CS) is a technique for estimating a sparse signal from the random measurements and the measurement matrix. Traditional sparse signal recovery methods have seriously degeneration with the measurement matrix uncertainty (MMU). Here the MMU is modeled as a bounded additive error. An anti-uncertainty constraint in the form of a mixed 2 and 1 norm is deduced from the sparse ...
متن کاملSparse signal recovery with OMP algorithm using sensing measurement matrix
Orthogonal matching pursuit (OMP) algorithm with random measurement matrix (RMM), often selects an incorrect variable due to the induced coherent interference between the columns of RMM. In this paper, we propose a sensing measurement matrix (SMM)-OMP which mitigates the coherent interference and thus improves the successful recovery probability of signal. It is shown that the SMM-OMP selects a...
متن کاملSparse signal recovery using sparse random projections
Sparse signal recovery using sparse random projections
متن کاملA signal recovery algorithm for sparse matrix based compressed sensing
We have developed an approximate signal recovery algorithm with low computational cost for compressed sensing on the basis of randomly constructed sparse measurement matrices. The law of large numbers and the central limit theorem suggest that the developed algorithm saturates the Donoho-Tanner weak threshold for the perfect recovery when the matrix becomes as dense as the signal size N and the...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Turkish Journal of Computer and Mathematics Education (TURCOMAT)
سال: 2021
ISSN: 1309-4653
DOI: 10.17762/turcomat.v12i6.1407