Computationally Efficient Sparse Reconstruction of Underwater Signals

Compressive sensing provides a means to reconstruct certain signals from fewer samples than the traditional methods use. Its popularity is increasing due to its promising reconstruction capabilities in various applications, such as speech processing, biomedical signal processing, underwater acoustic communication, etc.. Compressive sensing problems are usually handled with linear programming concepts or dynamic programming methods. This paper presents a specialized simple and computationally efficient method for the sparse reconstruction of underwater signals using fewer samples than are necessary for reconstruction in the traditional systems. The suitability of this method for efficient sparse reconstruction has been ascertained by using a wave file containing the underwater noise generated by a 3 blade engine.