Paper Info
Title
Distributed compressed sensing of non-negative signals using symmetric alpha-stable distributions
Abstract
Sensor networks gather an enormous amount of data over space and time to derive an estimate of a parameter or function. Several constraints, such as limited power, bandwidth, and storage capacity, motivate the need for a new paradigm for sensor data processing in order to extend the network's lifetime, while also obtaining accurate estimates. In a companion paper [1], we proposed a novel iterative algorithm for reconstructing non-negative sparse signals in highly impulsive background by modeling their prior distribution using symmetric alpha-stable distributions. In the present work, we extend this algorithm in the framework of distributed compressed sensing using duality theory and the method of subgradients for the optimization of the associated cost function. The experimental results show that our proposed distributed method maintains the reconstruction performance of its centralized counterpart, while also achieving a highly sparse basis configuration, thus reducing the total amount of data handled by each sensor.
Year
Venue
Keywords
2010
Aalborg
compressed sensing,optimisation,parameter estimation,signal reconstruction,associated cost function,distributed compressed sensing,duality theory,highly sparse basis configuration,iterative algorithm,nonnegative signals,nonnegative sparse signals reconstruction,optimization,sensor data processing networks,storage capacity,subgradient method,symmetric alpha-stable distributions,wireless sensor networks,linear programming,signal to noise ratio,sparse matrices
Field
DocType
ISSN
Mathematical optimization,Iterative method,Sparse approximation,Signal-to-noise ratio,Algorithm,Bandwidth (signal processing),Linear programming,Wireless sensor network,Sparse matrix,Compressed sensing,Mathematics
Conference
2219-5491
Citations 
PageRank 
References 
0
0.34
4
Authors
2
Name
Order
Citations
PageRank
George Tzagkarakis113917.94
P. Tsakalides2954120.69