A Sparsity Adaptive Subspace Pursuit Algorithm for Compressive Sampling

A novel sparsity adaptive subspace pursuit algorithm is proposed for sparse signals with unkown sparsity.First,a new sparsity estimation method based on matching test is used to get an initial estimated value.If subspace pursuit cannot reconstruct sparse signal successfully,the estimated value is increased each iteration.Weak matching is used in the iteration to select new atoms.Compared to other algorithms,it is competitive both in running speed and recovering accuracy.