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We consider the problem of in-network compressed sensing from distributed measurements. Every agent has a set of measurements of a signal , and the objective is for the agents to recover from their collective measurements using only communication with neighbors in the network. Our distributed approach to this problem is based on the centralized Iterative Hard Thresholding algorithm (IHT). We...
We consider the problem of in-network compressed sensing, where the goal is to recover a global, sparse signal from local measurements using only local computation and communication. Our approach to this distributed compressed sensing problem is based on the centralized Iterative Hard Thresholding algorithm (IHT). In time-varying networks, the network dynamics necessarily introduce inaccuracies that...
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