The A*orthogonal least square algorithm with the self-training dictionary for propeller signals reconstruction

The transmission of huge propeller signals via underwater wireless networks has high energy consumption, and conventional compression methods based on Nyquist sampling theorem cannot achieve efficient compression. Different from conventional compression methods, compressed sensing (CS) realizes the sub-Nyquist sampling rate, but the sparse signal is one of the preconditions of CS. Unfortunately, propeller signals are always non-sparse in both time and frequency domains. Therefore, the commonly used fixed dictionary is not suitable for sparse representation of propeller signals. To improve sparse representation, this paper proposes a self-training dictionary (STD) scheme, which updates the dictionary and sparse encoding simultaneously in iterations. Combining with the STD scheme, a CS reconstruction based on A*orthogonal least square (A*OLS) is proposed. In detail, a candidate subset selected by the orthogonal least square (OLS) algorithm is used as the node of the tree, and the search tree is established by A*search to find the optimal sparse solution. The experimental results confirm that the STD-A*OLS framework has significant advantages in performance including recovered signal-to-noise ratio (RSNR), percent norm difference (PND), and structure similarity index measure (SSIM).