نتایج جستجو برای: compressive sensing
تعداد نتایج: 145295 فیلتر نتایج به سال:
Traditional synthetic aperture radar (SAR) utilizes Shannon-Nyquist theorem for high bandwidth signal sampling, which induces a complicated SAR system, and it is difficult to transmit and process a huge amount of data caused by high A/D rate. Compressive sensing (CS) indicates that the compressible signal using a few measurements can be reconstructed by solving a convex optimization problem. A ...
In a sensor network with remote sensor devices, it is important to have a method that can accurately localize a sound event with a small amount of data transmitted from the sensors. In this paper, we propose a novel method for localization of a sound source using compressive sensing. Instead of sampling a large amount of data at the Nyquist sampling rate in time domain, the acoustic sensors tak...
Synthetic aperture radar (SAR) tomography (TomoSAR) extends the synthetic aperture principle into the elevation direction for 3-D imaging. The resolution in the elevation direction depends on the size of the elevation aperture, i.e., on the spread of orbit tracks. Since the orbits of modern meterresolution spaceborne SAR systems, like TerraSAR-X, are tightly controlled, the tomographic elevatio...
Compressive sensing (CS) is an emerging field that provides a framework for image recovery using sub-Nyquist sampling rates. The CS theory shows that a signal can be reconstructed from a small set of random projections, provided that the signal is sparse in some basis, e.g., wavelets. In this paper, we describe a method to directly recover background subtracted images using CS and discuss its a...
The computational ghost imaging with a phase spatial light modulator (SLM) for wave field coding is considered. A transmission-mask amplitude object is reconstructed from multiple intensity observations. Compressive techniques are used in order to gain a successful image reconstruction with a number of observations (measurement experiments), which is smaller than the image size. Maximum likelih...
Compressive sensing is a new field in signal processing and applied mathematics. It allows one to simultaneously sample and compress signals which are known to have a sparse representation in a known basis or dictionary along with the subsequent recovery by linear programming (requiring polynomial (P) time) of the original signals with low or no error [1–3]. Compressive measurements or samples ...
Based on compressive sensing framework and sparse reconstruction technology, a new pan-sharpening method, named Sparse Fusion of Images (SparseFI, pronounced as sparsify), is proposed in [1]. In this paper, the proposed SparseFI algorithm is validated using UltraCam and WorldView-2 data. Visual and statistic analysis show superior performance of SparseFI compared to the existing conventional pa...
0167-8655/$ see front matter 2012 Elsevier B.V. A doi:10.1016/j.patrec.2012.02.007 q This work is partially supported by Charles S Research Grant OPA 4818. 1 NICTA is funded by the Australian Government as re of Broadband, Communications and the Digital Econom Council through the ICT Centre of Excellence program. ⇑ Corresponding author. E-mail addresses: [email protected] (J. Gao), q Tiberio.Cae...
Due to increasing number of wireless services spectrum congestion is a major concern in both military and commercial wireless networks. To support growing demand for omnipresent spectrum usage, Cognitive Radio is a new epitome in wireless communication that can be used to exploit unused part of the spectrum by dynamically adjusting its operating parameters. While cognitive radio technology is a...
Compressive sampling (CS), or compressive sensing, has the ability for reconstructing a sparse signal with small number of measurements. There are some applications like spectrum sensing in cognitive radio which not necessarily need a perfect reconstruction. Consequently in this application, toward the decrement of high signal acquisition costs in wideband system, CS methods have been used for ...
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