光学学报, 2016, 36 (6): 0614002, 网络出版: 2016-05-25  

半导体激光器光谱局部最大峰值检索算法

Local Maximum Algorithm for Detecting Spectral Peaks of Semiconductor Lasers
作者单位
中国电子科技集团公司第四十一研究所电子测试技术重点实验室, 山东 青岛 266555
摘要
通过分析实测法布里-珀罗型半导体激光器(FP-LD)和分布反馈式半导体激光器(DFB-LD)光谱数据特征,提出了一种基于局部最大矩阵的自适应算法,用于检索半导体激光器光谱峰值。建立数据序列的局部最大矩阵,利用矩阵行向量特征修正该矩阵,根据修正后矩阵的列向量特征定位峰值,并进一步修正和补偿峰值位置。该算法具有较强的抗噪能力,整个检索过程无需人为干预,自适应性和稳健性强,满足实时计算的要求。实测数据计算结果表明,该算法与直接比较法、优化的导数法和遗传算法相比,在检验准确率和计算时间方面优势明显,平均检验准确率可达98%,平均计算时间仅为0.12 s,可应用于半导体激光器实测光谱特性实时分析。
Abstract
A self-adaptive local maximum algorithm for detecting spectral peaks of semiconductor lasers is proposed by analyzing actually measured spectrum characteristics of Fabry-Perot laser diode (FP-LD) and distributed feedback laser diode (DFB-LD), and the algorithm can be used to detect the spectra of laser diodes. In the algorithm, a local maximum matrix of data series is established and is modified with its row vector characteristics, and the peak is located with its column vector characteristics that are further amended and compensated. The algorithm is free from manual operation during the whole searching process, has higher noise-resistance, self-adaptability and robustness, and meets the requirements of real-time computation. Compared with the comparison algorithm, optimized derivative algorithm and genetic algorithm, the local maximum algorithm has obvious advantages in terms of accuracy rate and computation time. The average test accuracy rate reaches 98% while the average computation time is only 0.12 s. The algorithm can be applied to real-time analysis of actually measured spectrum characteristics of semiconductor lasers.
参考文献

[1] 段慧. 基于速率方程的半导体激光器响应特性研究[D]. 秦皇岛: 燕山大学, 2010: 1-9.

    Duan Hui. The response characteristics of semiconductor lasers based on rate equations[D]. Qinhuangdao: Yanshan University, 2010: 1-9.

[2] Campos J M, Destrez A, Jacquet J, et al.. Ultra-fast optical spectrum analyzer for DWDM applications[J]. IEEE Transactions on Instrumentation and Measurement, 2004, 53(1): 124-129.

[3] Jacobson M L. Auto-threshold peak detection in physiological signals[C]. Proceedings of the 23rd Annual International Conference of the Engineering in Medicine and Biology Society, IEEE, 2001: 2194-2195.

[4] 陈亮. 核素识别算法及数字化能谱采集系统研究[D]. 北京: 清华大学, 2009: 34-37.

    Chen Liang. Research on the nuclide identification algorithm and digital spectra acquisition system[D]. Beijing: Tsinghua University, 2009: 34-37.

[5] 王巧妮, 杨远洪. 基于Steger图像算法的光纤布拉格光栅寻峰技术[J]. 光学学报, 2014, 34(8): 0810004.

    Wang Qiaoni, Yang Yuanhong. A FBG spectrum peak detection technique based on Steger image algorithm[J]. Acta Optica Sinica, 2014, 34(8): 0810004.

[6] 陈勇, 杨凯, 刘焕淋. 多峰光纤布拉格光栅传感信号的自适应寻峰处理[J]. 中国激光, 2015, 42(8): 0805008.

    Chen Yong, Yang Kai, Liu Huanlin. A self-adaptive peak detection algorithm to process multi-peak fiber Bragg grating sensing signal[J]. Chinese J Lasers, 2015, 42(8): 0805008.

[7] 毕云峰, 李颖, 郑荣儿. LIBS/Raman光谱对称零面积变换自动寻峰方法研究[J]. 光谱学与光谱分析, 2013, 33(2): 438-443.

    Bi Yunfeng, Li Ying, Zheng Ronger. The symmetric zero-area conversion adaptive peak-seeking method research for LIBS/Raman spectra[J]. Spectroscopy and Spectral Analysis, 2013, 33(2): 438-443.

[8] 易龙涛, 刘志国, 陈曼, 等. 一种X射线荧光光谱数据批处理新方法的研究[J]. 激光与光电子学进展, 2014, 51(7): 073001.

    Yi Longtao, Liu Zhiguo, Chen Man, et al.. A new automatic analysis method for X-ray fluorescence spectrometric qualitative analysis[J]. Laser & Optoelectronics Progress, 2014, 51(7):073001.

[9] 王霖郁, 康新. 基于种群优化的遗传算法的MUSIC谱峰搜索技术[J]. 计算机应用研究, 2014, 31(12): 3543-3545.

    Wang Linyu, Kang Xin. Research on MUSIC spectral peak searching based on improved population genetic algorithms[J]. Application Research of Computers, 2014, 31(12): 3543-3545.

[10] Singh O, Sunkaria K R. A robust R-peak detection algorithm using wavelet packets[J]. International Journal of Computer Applications, 2011, 36(5): 37-43.

[11] 陈鹏飞, 田地, 乔淑君, 等. 一种基于连续小波变换的LIBS光谱自动寻峰方法[J]. 光谱学与光谱分析, 2014, 34(7): 1969-1972.

    Chen Pengfei, Tian Di, Qiao Shujun, et al.. An automatic peak detection method for LIBS spectrum based on continuous wavelet transform[J]. Spectroscopy and Spectral Analysis, 2014, 34(7): 1969-1972.

[12] 刘康. 微型光谱仪关键技术及其应用研究[D]. 杭州: 浙江大学, 2013: 88-90.

    Liu Kang. Research on the key technology of the miniature spectrometer and its application system[D]. Hangzhou: Zhejiang University, 2013: 88-90.

[13] 汤斌, 魏彪, 毛本将, 等. 紫外-可见吸收光谱法水质检测系统的噪声分析与处理研究[J]. 激光与光电子学进展, 2014, 51(4): 043002.

    Tang Bin, Wei Biao, Mao Benjiang, et al.. Noise analysis and denoising research on the UV-visible absorption spectroscopy water quality detection system[J]. Laser & Optoelectronics Progress, 2014, 51(4): 043002.

[14] 王书涛, 曾秋菊, 宋浩兵, 等. 基于SVM滤波器的吸收式甲烷检测的信号去噪方法[J]. 中国激光, 2014, 41(9): 0915001.

    Wang Shutao, Zeng Qiuju, Song Haobing, et al.. Signal denoising method based on the SVM filter absorption methane detection[J]. Chinese J Lasers, 2014, 41(9): 0915001.

[15] 纪越峰. 现代光纤通信技术[M]. 北京: 人民邮电出版社, 1997: 160-162.

    Ji Yuefeng. Modern optical fiber communication technology[M]. Beijing: People′s Posts and Telecommunications Press, 1997: 160-162.

[16] 韩军. 光学膜厚宽带监控关键技术研究[D]. 西安: 西安电子科技大学, 2011: 31-33.

    Han Jun. Research on key technology in thin-film thickness wideband monitoring system[D]. Xi′an: Xidian University, 2011: 31-33.

侯喜报, 刘加庆, 张志辉, 韩顺利. 半导体激光器光谱局部最大峰值检索算法[J]. 光学学报, 2016, 36(6): 0614002. Hou Xibao, Liu Jiaqing, Zhang Zhihui, Han Shunli. Local Maximum Algorithm for Detecting Spectral Peaks of Semiconductor Lasers[J]. Acta Optica Sinica, 2016, 36(6): 0614002.

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