关键词: 压缩感知, 稀疏度, 支撑集

Abstract: The key of reconstruction algorithm based on CS is to exactly resume the original high-dimension-data from low-dimension measurement. Aiming at most algorithms acquiring the sparsity as a prior, a backward and fixed step size adaptive matching pursuit reconstruction algorithm is presented, which can acquire exact reconstruction without prior information of sparsity. A fixed and biggish step size is set to make sure the size of support set of the signal to be increased stably and quickly. The energy difference between adjacent reconstructed signals is taken as the halting condition for iteration. A standard regularized method is employed to post-dispose the final iteration result, which backward eliminates superfluous atoms to acquire exact reconstruction. Experimental results show that this algorithm can quickly acquire exact reconstruction with sufficient measurements.

Key words: compressed sensing, sparsity, support set