Self-adapting peak search method for spectrum of semiconductor laser

Abstract

The invention discloses a self-adapting peak search method for a spectrum of a semiconductor laser. The self-adapting peak search method comprises the following steps of: establishing a local maximum matrix of a spectrum data sequence, correcting the matrix with a row vector feature, positioning a peak value according to a column vector feature of the corrected matrix, and performing further correction and compensation with a certain method. The method has relatively good noise resistance, and the whole search process is free of human intervention, so that the adaptivity and the robustness are high and real-time calculation requirements are met. An actually measured data calculation result shows that compared to a direct comparison method, an optimized derivative method and a genetic algorithm, the self-adapting peak search method has the characteristics that the advantages in check accuracy and calculation time are obvious, the average check accuracy can reach 98%, the average calculation time is only 0.12 seconds, and the self-adapting peak search method can be applied to real-time analysis of actually measured spectrums 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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