Atom selection strategy for signal compressed recovery based on sensing information entropy

In greedy pursuit algorithm, atom selection is commonly a concerned topic for signal compressed recovery. To improve the recovery performance, an optimal atom selection strategy without the prior information is proposed in this paper. The sensing information entropy is defined to prune the possible false atoms in the estimated support set. Fewer iterations are required in the proposed strategy and it can also be applied in the case with high sparsity level or low signal-noise-ratio. Compared with the existing representative algorithms, the superiority of the recovery error and probability is verified by the simulations. Furthermore, the proposed method is applied to recover the real random modulated signal. The results show that the recovered signal has greater consistence with the original input signal.