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This paper proposes a fast pursuit method for greedy algorithms when reconstructing multi-signals under Distributed Compressive Sensing (DCS) framework. DCS takes advantage of both intra- and inter-signal correlation structures to reduce the measurements required for signals recovery. Greedy algorithms, much faster than l0 and l1 minimization algorithms, are widely used in DCS. General approaches...
Distributed Compressive Sensing (DCS) theory effectively reduces the number of measurements of each signal, by exploiting both intra- and inter-signal correlation structures, which saves on the costs of sampling devices as well as of communication and data processing. In many fields, only the mixtures of source signals are available for compressive sampling, without prior information on both the source...
Compressive sensing (CS) shows that, when a signal is sparse or compressible with respect to some basis, a small number of compressive measurements of the original signal can be sufficient for exact (or approximate) recovery. Distributed CS (DCS) takes advantage of both intra- and intersignal correlation structures to reduce the number of measurements required for multisignal recovery. In most cases...
Multiband signal is a typical signal in the realm of modern communication, whose spectrum is the sum of several narrow band signals in frequency domain. Modulated Wideband Converter (MWC) system, which is based on the emerging theory of Compressed Sensing (CS), can sample multiband signals at a sub-Nyquist rate without carrier frequencies as a precondition. In this paper, a novel recovery strategy...
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