A Spectral Adaptive Peak Search Method for Semiconductor Lasers

Abstract

The invention discloses a semiconductor laser spectrum self-adaptive peak search method. The self-adaptive peak search method comprises the following steps: by establishing a local maximum matrix of the spectral data sequence, using its row vector feature to modify the matrix, and according to the column vector feature of the correction matrix Locate the peak, and further correct and compensate through certain methods. The method of the invention has strong anti-noise ability, the whole search process does not need human intervention, has strong adaptability and robustness, and meets the requirement of real-time calculation. The calculation result of measured data shows that compared with the direct comparison method, the optimized derivative method and the genetic algorithm, the self-adaptive peak search method of the present invention has obvious advantages in terms of inspection accuracy and calculation time, and the average inspection accuracy can reach 98%. The time is only 0.12s, which can be applied to the real-time analysis of the measured spectral characteristics of semiconductor lasers.

Description

technical field [0001] The invention relates to a semiconductor laser spectrum self-adaptive peak search method. Background technique [0002] At present, due to the advantages of large transmission capacity, high transmission rate, anti-electromagnetic interference, and strong confidentiality, optical fiber communication technology has been rapidly developed and widely used in various fields such as military, industry, agriculture, medical treatment, and scientific research. Fabry-Perot semiconductor lasers (FP-LD) and distributed feedback semiconductor lasers (DFB-LD) have good output light coherence and directivity, narrow spectral width, small size and ease of use. It is the most commonly used light source in optical fiber communication systems, and its spectrum is usually measured by a spectrum analyzer. Parameters such as spectral bandwidth and longitudinal mode number are the most important spectral parameters of FP-LD, and parameters such as side-mode suppression rat...

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Problems solved by technology

The wavelet transform method decomposes the signal into a superposition of a series of wavelet functions, and constructs a time-frequency signal with good localization in the time domain and frequency domain, thus transforming the peak-seeking in the time-domain into the peak-finding of the wavelet coefficient matrix in the wavelet domain. The accuracy is high, and it has a strong ability to suppress noise and background, but this method needs to choose wavelet mother function, scale parameter, translation parameter, etc., the calculation amount is large, and it is not suitable for real-time operation

[0004] It can be seen that the following problems exist in the existing methods for peak search of the above-mentioned semiconductor laser spectra: (1) In order to obtain higher search accuracy, most methods need to be set according to different types of spectral waveforms obtained under different measurement conditions Different optimal thresholds or parameters require more human intervention, the method cannot be self-adaptive, and the robustness is poor; (2) In order to filter out false peaks, preprocessing such as filter denoising, smoothing, and background baseline drift removal is required , not only the steps are cumbersome and the calculation is complicated, but also new thresholds or parameters are inevitably introduced in the preprocessing process; (3) The method itself has a relatively large calculation time cost, and considering the time spent on data preprocessing, it cannot satisfy real-time calculations Require

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