SGCS: a signal reconstruction method based on Savitzky-Golaysgz filtering and compressed sensing for wavelength...

The residual oxygen concentration in pharmaceutical glass vial variously threatens the aseptic properties of encapsulated agents. The demodulated 2nd harmonic signals in the wavelength modulation spectroscopy (WMS) detection system, the data basis of the inversion of oxygen concentration, are inevitably destroyed by various time-varying industrial noises. In this work, we propose a signal reconstruction method based on self-correcting Savitzky-Golaysgz filter and compressed sensing (namely SGCS) for the urgent signal denoising task, which is a dual-step lightweight denoising scheme. First, in order to avoid the influence of glitch noise on sparse signal reconstruction, Savitzky-Golay (S-G) filter is used to smooth the 2nd harmonic signal while retaining the change information effectively. Then, the well-tuned measurement matrix of compressed-sensing (CS) is applied to aggressively fetch the sparse principal components while bypassing most residual dynamic noises. Finally, the orthogonal matching pursuit (OMP) is used to reconstruct the 2nd harmonic signal according to sparsity constrain and the sparse principal components. Experimental results show that the performance of SGCS method is superior. Compared with other competitive methods the operation time of SGCS is the shortest. When the normalized SNR is 1, the average correct discrimination rate is 98.57%. Even if SNR reduces from 1 to 0.55, the WMS detection system still survives well, with the highest average correct discrimination rate of 89.34%.