Spectrum denoising method

Abstract

The invention discloses a spectrum denoising method which comprises the following steps: acquiring a plurality of groups of spectrum signal samples; setting an order number and a regularization coefficient of a self-adaptive filter, selecting a minimum mean square error function as an optimal target function of the filter, and taking the samples as input signals of the filter so as to obtain output signals; based on a minimum mean square error function corresponding to a same position n of k samples, acquiring a weight coefficient vector W of the self-adaptive filter according to an Adam algorithm; calculating a signal to noise ratio of the self-adaptive filter; within a preset range of the order number and the regularization coefficient, updating the order number and the regularization coefficient of the self-adaptive filter, repeating the step of acquiring the signal to noise ratio of each self-adaptive filter, and selecting a self-adaptive filter corresponding to the maximum singleto noise ratio; performing filtering denoising on a same type of spectrum signals under a same environment condition by using the selected self-adaptive filter. Compared with a conventional standard LMS algorithm, the method disclosed by the invention is optimal in denoising effect, and rapid in convergence rate.

Description

technical field [0001] The invention belongs to the technical field of spectral signal processing, and in particular relates to a spectral denoising method. Background technique [0002] When using ultraviolet-visible spectroscopy to detect trace heavy metal ions in a high concentration ratio background, the measured absorption spectrum signals often contain a lot of interference information. Compared with the spectral signal amplitude of high-concentration zinc, the spectral signal amplitude of trace multiple metal ions is small, and it is very susceptible to noise interference. Therefore, denoising is very important for data processing and analysis, and directly affects subsequent quantitative analysis and information mining. The key to improving the accuracy of spectral analysis and improving the ability of spectral analysis is to choose the appropriate denoising method. [0003] Now commonly used spectral filtering algorithms mainly include wavelet transform algorithm,...

AI Technical Summary

Problems solved by technology

However, many parameters of these filtering algorithms need to be determined manually, and cannot achieve the denoising effect adaptively.

Although the standard LMS algorithm can dynamically adjust the filter coefficients according to the minimum mean square error criterion to achieve the purpose of adaptive denoising, but the convergence speed is not fast enough, and improper parameter selection is prone to overfitting

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