Joint compressive spectrum sensing scheme in wideband cognitive radio networks

In this paper,a distributed compressive spectrum sensing scheme in wideband cognitive radio networks is investigated.An analog-to-information converters（AIC） RF front-end sampling structure is proposed which use parallel low rate analog to digital conversions（ADCs） and fewer storage units for wideband spectrum signal sampling.The proposed scheme uses multiple low rate congitive radios（CRs） collecting compressed samples through AICs distritbutedly and recover the signal spectrum jointly.A general joint sparsity model is defined in this scenario,along with a universal recovery algorithm based on simultaneous orthogonal matching pursuit（S-OMP）.Numerical simulations show this algorithm outperforms current existing algorithms under this model and works competently under other existing models.

A survey on distributed compressed sensing： theory and applications

The compressed sensing （CS） theory makes sample rate relate to signal structure and content. CS samples and compresses the signal with far below Nyquist sampling frequency simultaneously. However, CS only considers the intra-signal correlations, without taking the correlations of the multi-signals into account. Distributed compressed sensing （DCS） is an extension of CS that takes advantage of both the inter- and intra-signal correlations, which is wildly used as a powerful method for the multi-signals sensing and compression in many fields. In this paper, the characteristics and related works of DCS are reviewed. The framework of DCS is introduced. As DCS＇s main portions, sparse representation, measurement matrix selection, and joint reconstruction are classified and summarized. The applications of DCS are also categorized and discussed. Finally, the conclusion remarks and the further research works are provided.

ACCURATE AND EFFICIENT IMAGE RECONSTRUCTION FROM MULTIPLE MEASUREMENTS OF FOURIER SAMPLES

Several problems in imaging acquire multiple measurement vectors(MMVs)of Fourier samples for the same underlying scene.Image recovery techniques from MMVs aim to exploit the joint sparsity across the measurements in the sparse domain.This is typically accomplished by extending the use of`1 regularization of the sparse domain in the single measurement vector(SMV)case to using`2,1 regularization so that the“jointness”can be accounted for.Although effective,the approach is inherently coupled and therefore computationally inefficient.The method also does not consider current approaches in the SMV case that use spatially varying weighted`1 regularization term.The recently introduced variance based joint sparsity(VBJS)recovery method uses the variance across the measurements in the sparse domain to produce a weighted MMV method that is more accurate and more efficient than the standard`2,1 approach.The efficiency is due to the decoupling of the measurement vectors,with the increased accuracy resulting from the spatially varying weight.Motivated by these results,this paper introduces a new technique to even further reduce computational cost by eliminating the requirement to first approximate the underlying image in order to construct the weights.Eliminating this preprocessing step moreover reduces the amount of information lost from the data,so that our method is more accurate.Numerical examples provided in the paper verify these benefits.

Robust PCA for Ground Moving Target Indication in Wide-Area Surveillance Radar System

Robust PCA has found important applications in many areas,such as video surveillance,face recognition,latent semantic indexing and so on.In this paper,we study its application in ground moving target indication(GMTI)in wide-area surveillance radar system.MTI is the key task in wide-area surveillance radar system.Due to its great importance in future reconnaissance systems,it attracts great interest from scientists.In(Yan et al.in IEEE Geosci.Remote Sens.Lett.,10:617–621,2013),the authors first introduced robust PCA to model the GMTI problem,and demonstrate promising simulation results to verify the advantages over other models.However,the robust PCA model can not fully describe the problem.As pointed out in(Yan et al.in IEEE Geosci.Remote Sens.Lett.,10:617–621,2013),due to the special structure of the sparse matrix(which includes the moving target information),there will be difficulties for the exact extraction of moving targets.This motivates our work in this paper where we will detail the GMTI problem,explore the mathematical properties and discuss how to set up better models to solve the problem.We propose two models,the structured RPCA model and the row-modulus RPCA model,both of which will better fit the problem and take more use of the special structure of the sparse matrix.Simulation results confirm the improvement of the proposed models over the one in(Yan et al.in IEEE Geosci.Remote Sens.Lett.,10:617–621,2013).