The Sampling Rate-Distortion Tradeoff for Sparsity Pattern Recovery in Compressed Sensing
نویسندگان
چکیده
منابع مشابه
Sparsity Pattern Recovery in Compressed Sensing
Sparsity Pattern Recovery in Compressed Sensing by Galen Reeves Doctor of Philosophy in Engineering — Electrical Engineering and Computer Sciences University of California, Berkeley Professor Michael Gastpar, Chair The problem of recovering sparse signals from a limited number of measurements is now ubiquitous in signal processing, statistics, and machine learning. A natural question of fundame...
متن کاملCompressed Sensing: “When sparsity meets sampling”∗
The recent theory of Compressed Sensing (Candès, Tao & Romberg, 2006, and Donoho, 2006) states that a signal, e.g. a sound record or an astronomical image, can be sampled at a rate much smaller than what is commonly prescribed by Shannon-Nyquist. The sampling of a signal can indeed be performed as a function of its “intrinsic dimension” rather than according to its cutoff frequency. This chapte...
متن کاملA Sharp Sufficient Condition for Sparsity Pattern Recovery
Sufficient number of linear and noisy measurements for exact and approximate sparsity pattern/support set recovery in the high dimensional setting is derived. Although this problem as been addressed in the recent literature, there is still considerable gaps between those results and the exact limits of the perfect support set recovery. To reduce this gap, in this paper, the sufficient con...
متن کاملUnmanned aerial vehicle field sampling and antenna pattern reconstruction using Bayesian compressed sensing
Antenna 3D pattern measurement can be a tedious and time consuming task even for antennas with manageable sizes inside anechoic chambers. Performing onsite measurements by scanning the whole 4π [sr] solid angle around the antenna under test (AUT) is more complicated. In this paper, with the aim of minimum duration of flight, a test scenario using unmanned aerial vehicles (UAV) is proposed. A pr...
متن کاملIterative methods for random sampling and compressed sensing recovery
In this paper, two methods are proposed which address the random sampling and compressed sensing recovery problems. The proposed random sampling recovery method is the Iterative Method with Adaptive Thresholding and Interpolation (IMATI). Simulation results indicate that the proposed method outperforms existing random sampling recovery methods such as Iterative Method with Adaptive Thresholding...
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: IEEE Transactions on Information Theory
سال: 2012
ISSN: 0018-9448,1557-9654
DOI: 10.1109/tit.2012.2184848