Visible to the public Exact Sparse Signal Recovery via Orthogonal Matching Pursuit with Prior Information

TitleExact Sparse Signal Recovery via Orthogonal Matching Pursuit with Prior Information
Publication TypeConference Paper
Year of Publication2019
AuthorsWen, Jinming, Yu, Wei
Conference NameICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Date Publishedmay
KeywordsClosed-form solutions, composability, compressed sensing, computer security, exact recovery probability, exact sparse signal recovery, gaussian distribution, greedy algorithms, Indexes, Iterative methods, K-sparse signals, linear model, Matching pursuit algorithms, matrix algebra, Metrics, nonzero entries, OMP, orthogonal matching pursuit (OMP), orthogonal matching Pursuit algorithm, prior distribution information, prior information, privacy, probability, pubcrawl, random matrix, Resiliency, sensing matrix, Sensors, signal processing security, Upper bound
AbstractThe orthogonal matching pursuit (OMP) algorithm is a commonly used algorithm for recovering K-sparse signals x Rn from linear model y = Ax, where A Rmxn is a sensing matrix. A fundamental question in the performance analysis of OMP is the characterization of the probability that it can exactly recover x for random matrix A. Although in many practical applications, in addition to the sparsity, x usually also has some additional property (for example, the nonzero entries of x independently and identically follow the Gaussian distribution), none of existing analysis uses these properties to answer the above question. In this paper, we first show that the prior distribution information of x can be used to provide an upper bound on \textbackslashtextbar\textbackslashtextbarx\textbackslashtextbar\textbackslashtextbar21/\textbackslashtextbar\textbackslashtextbarx\textbackslashtextbar\textbackslashtextbar22, and then explore the bound to develop a better lower bound on the probability of exact recovery with OMP in K iterations. Simulation tests are presented to illustrate the superiority of the new bound.
DOI10.1109/ICASSP.2019.8682166
Citation Keywen_exact_2019