Research On Greedy Reconstruction Algorithm Of Compressed Sensing

With the extension of intelligent wireless sensor network and the countless sensor products coming into service in 21 th century.The information collected by the sensor equipment brought great difficulties to the rapid development of information processing.For traditional methods,most of the sensors collected information with the guidance of The Nyquist sampling theorem,while the information collection technology of The Nyquist sampling theorem imposed restrictions on the sampling rate of minimum information,this technology is the sufficient condition bu not the necessary condition for the reconstruction of original data signal.In recent years,the proposal of compress sensor technology pushed the limitation of minimum information sample rate triggered by Nyquist sampling information collection technology and compress sensor technology got the observed signal on the basis of low information sample rate of signal sparseness,and the technology also reconstruct the observed signal into original signal according to the existing reconstructing algorithm.In this paper,a matching pursuit based on hope-tree algorithm and an adaptive subspace pursuit algorithm are proposed.The research work of this paper is as follows:Putting forward the matching pursuit based on hope-tree(GSRA).First,we obtain a good initial support set by run the orthogonal matching pursuit(OMP)algorithm and the subspace pursuit(SP)algorithm,then a hope-tree is built and candidate support extended by searching the tree,which greatly reduces the complexity of breadth first search.Finally,the subspace pursuit method rectifies candidate support set.The complexity of GSRA is aboutmaxO(N mn?iter),and the reason why at least one correct index in child nodes is presented.The simulation results show that the GSRA has a better performance for Gauss signal,0-1 signal and noise signal.Putting forward the adaptive subspace pursuit algorithm(ASP).In this algorithm,the index are selected by the condition of hard threshold,and the support set is extended and the residuals are updated.The main feature of this algorithm is that the structure is simple and the complexity of the ASP is about O(mn?K),which is the same magnitude with the OMP.The simulation experiments show that when the sparsity is m/ 2,reconstructive accuracy of the OMP and the SP are 0,however,theASP is 20%.When the sampling rate is low,the effect of reconstruction of noisy signal is better than the OMP and the SP.