1 research outputs found
Diffusion Adaptation Framework for Compressive Sensing Reconstruction
Compressive sensing(CS) has drawn much attention in recent years due to its
low sampling rate as well as high recovery accuracy. As an important procedure,
reconstructing a sparse signal from few measurement data has been intensively
studied. Many reconstruction algorithms have been proposed and shown good
reconstruction performance. However, when dealing with large-scale sparse
signal reconstruction problem, the storage requirement will be high, and many
algorithms also suffer from high computational cost. In this paper, we propose
a novel diffusion adaptation framework for CS reconstruction, where the
reconstruction is performed in a distributed network. The data of measurement
matrix are partitioned into small parts and are stored in each node, which
assigns the storage load in a decentralized manner. The local information
interaction provides the reconstruction ability. Then, a simple and efficient
gradient-descend based diffusion algorithm has been proposed to collaboratively
recover the sparse signal over network. The convergence of the proposed
algorithm is analyzed. To further increase the convergence speed, a mini-batch
based diffusion algorithm is also proposed. Simulation results show that the
proposed algorithms can achieve good reconstruction accuracy as well as fast
convergence speed