A fast subspace pursuit for compressive sensing

A fast subspace pursuit algorithm is proposed to reconstruct sparse signals for compressive sensing. In order to reduce the computational cost, we symbolize the sampling matrix. As the result, multiplication is not required in the step of calculating the correlation. The analysis and simulation results reveal that the computational complexity of the proposed algorithm is much lower than that of the original subspace pursuit algorithm whereas the performance loss is fairly acceptable.