The Joint Reconstruction Algorithms Research Of Distributed Compressed Sensing

Compressed sensing is a new signal sampling theory which makes full use of the signal sparsity and finishes sampling and compressing at the sametime. Distributed compressed sensing established on the basis of compressed sensing. It can exploit both intra- and inter- signal correlation structures at the same time which makes compression and reconstruction from a single signal to expansion of multi-signals and implement multi-signals distributed compression and reconstruction. Based on distributed compressed sensing of joint reconstruction algorithm, this paper conducts the following research aspects.First, focusing on the mixed support-set model of distributed compressed sensing,joint look ahead variable stepsize orthogonal matching pursuit algorithm is brought forward. The algorithm dynamically performs the adaptive adjustment of forward parameters according to the energy difference between reconstructed signals of adjacent iterations to strike a balance between signal reconstruction accuracy and its running time.Furthermore, a joint forward-backward variable stepsize orthogonal matching pursuit algorithm is put forward. The algorithm effectively reduces the chance of choosing non-optimal atoms and improves the signal reconstruction accuracy.Then, considering the disadvantages of ignoring signal’s structured sparsity and the high complexity in high iterative layers in multipath matching pursuit, block pruning multipath matching pursuit is proposed to reconstruct block-sparse signal. In this algorithm, an atomic block serves as a node in the path expansion, and branch pruning operation is introduced after a certain number of iterations. Thus, block pruning multipath matching pursuit reduces the data processing cost greatly. Moreover, for multiple measurement vector problem, block pruning multipath matching pursuit for multiple measurement vector is proposed. It can achieve joint signal reconstruction for multiple sensors within a small range in the wireless sensor network. Experimental results show that the proposed algorithm has advantages in reconstruction effect and running time.Finally, aiming at hierarchical distributed compressed sensing for clustering wirelesssensor network, block-sparse intra-cluster joint sparsity model is constructed to describe the temporal-spatial correlation among data collected by cluster members, and a block-sparse inter-cluster joint sparsity model is constructed to describe the spatial relationship among clusters arranged in a common monitoring area. Then, a hierarchical measurement scheme and a hierarchical joint reconstruction scheme are raised for hierarchical distributed compressed sensing, based on the structural sparsity characteristics of data collected in clustering wireless sensor network. Experimental results show the program can reduce the amount of data transmitted in the network and relieve the transmission burden on the cluster heads effectively, without lowering the quality of the reconstructed signal.