Salient space detection algorithm for signal extraction from contaminated and distorted spectrum. Issue 11 (14th May 2018) - Jia, Y. W.; Sun, S. Y.; Yang, L.; Wang, D.
Abstract : The proposed algorithm minimizes the influence of baseline distortion and exhibits good anti-noise ability and high real-time performance. Abstract : An algorithm for signal extraction from a contaminated and distorted spectrum is proposed. First, this algorithm combines the salient space of the spectrum and the statistical characteristics of the noise to detect signal regions at different scales. Second, it extracts signals by subtracting the baseline from the spectrum in the signal regions. The baseline is fitted by segmented polynomial functions. This algorithm has been applied to simulated and experimental data, and the results show that this algorithm can accurately and automatically extract signals with varying widths from a contaminated spectrum. This method minimizes the influence of baseline distortion and exhibits good anti-noise capability and high real-time performance.