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Atom selection strategy for signal compressed recovery based on sensing information entropy
ISA Transactions ( IF 7.3 ) Pub Date : 2020-12-28 , DOI: 10.1016/j.isatra.2020.12.050
Xinyu Dao , Min Gao , Yi Wang

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.



中文翻译:

基于感知信息熵的信号压缩恢复的原子选择策略

在贪婪追踪算法中,原子选择通常是信号压缩恢复的一个关注主题。为了提高恢复性能,本文提出了一种没有先验信息的最佳原子选择策略。感知信息熵被定义为修剪估计支持集中可能的假原子。所提出的策略需要较少的迭代,也可以应用于高稀疏度或低信噪比的情况。与现有的代表性算法相比,通过仿真验证了恢复误差和概率的优越性。此外,所提出的方法被应用于恢复实随机调制信号。结果表明,恢复后的信号与原始输入信号具有较大的一致性。

更新日期:2020-12-28
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