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For compressed sensing over arbitrarily connected networks, we consider the problem of estimating underlying sparse signals in a distributed manner. We introduce a new signal model that helps to describe inter-signal correlation among connected nodes. Based on this signal model along with a brief survey of existing greedy algorithms, we develop distributed greedy algorithms with low communication...
We develop a greedy (pursuit) algorithm for a distributed compressed sensing problem where multiple sensors are connected over a de-centralized network. The algorithm is referred to as distributed parallel pursuit and it solves the distributed compressed sensing problem in two stages; first by a distributed estimation stage and then an information fusion stage. Along with worst case theoretical analysis...
In a compressed sensing setup with jointly sparse, correlated data, we develop a distributed greedy algorithm called distributed predictive subspace pursuit. Based on estimates from neighboring sensor nodes, this algorithm operates iteratively in two steps: first forming a prediction of the signal and then solving the compressed sensing problem with an iterative linear minimum mean squared estimator...
We develop a greedy pursuit algorithm for solving the distributed compressed sensing problem in a connected network. This algorithm is based on subspace pursuit and uses the mixed support-set signal model. Through experimental evaluation, we show that the distributed algorithm performs significantly better than the standalone (disconnected) solution and close to a centralized (fully connected to a...
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