Weak Coherent Light Interference Heterodyne Detection Based on Time-Domain Signal Analysis

IF 1.4 4区 工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC
Hui Shen, Yousen Li
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引用次数: 0

Abstract

Weak coherent light interference heterodyne detection is the theoretical basis for fiber optic gyroscopes, optical coherence tomography, and optical time-domain reflectometers. Classical statistical optics provides the signal model for weak coherent light interference. However, this theory does not describe signal acquisition and nonpolarization, which are significant in the analysis of heterodyne detection frequency, coherent length, and polarization mode dispersion (PMD). Consequently, it has difficulty solving signal processing problems related to coherent frequency and length analysis. This article proposed a time-domain signal analysis method. The approach can describe the practical signal acquisition and the polarized direction interference and accurately obtain coherent frequency and length on weak coherent light interference heterodyne detection signals by integrating the interference signals of monochromatic light within the linewidth of weak coherent light. We obtained the final mode of the signals using MATLAB. We established an experimental system to validate the practical value of the approach in signal processing. The average deviation between the experimental and theoretical coherent frequency and length is 120.6 Hz/0.48% and −0.0072 μm/−0.06%, respectively. Compared with existing theory, the proposed method is advantageous for describing detector acquisition and has practical value in heterodyne detection analysis. The proposed method can be widely applied to the systems based on weak coherent interference.

Abstract Image

基于时域信号分析的弱相干光干涉外差检测
弱相干光干涉外差检测是光纤陀螺仪、光相干层析成像和光时域反射仪的理论基础。经典统计光学为弱相干光干涉提供了信号模型。然而,该理论没有描述信号采集和非极化,这在外差检测频率、相干长度和偏振模色散(PMD)的分析中是重要的。因此,它很难解决与相干频率和长度分析有关的信号处理问题。本文提出了一种时域信号分析方法。该方法通过对弱相干光线宽范围内的单色光干涉信号进行积分,可以描述实际信号采集和极化方向干扰,准确地获得弱相干光干涉外差检测信号的相干频率和相干长度。利用MATLAB得到了信号的最终模态。建立了实验系统,验证了该方法在信号处理中的实用价值。实验和理论的相干频率和长度的平均偏差分别为120.6 Hz/0.48%和- 0.0072 μm/ - 0.06%。与现有理论相比,该方法有利于描述探测器采集,在外差检测分析中具有实用价值。该方法可广泛应用于基于弱相干干涉的系统。
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来源期刊
IET Signal Processing
IET Signal Processing 工程技术-工程:电子与电气
CiteScore
3.80
自引率
5.90%
发文量
83
审稿时长
9.5 months
期刊介绍: IET Signal Processing publishes research on a diverse range of signal processing and machine learning topics, covering a variety of applications, disciplines, modalities, and techniques in detection, estimation, inference, and classification problems. The research published includes advances in algorithm design for the analysis of single and high-multi-dimensional data, sparsity, linear and non-linear systems, recursive and non-recursive digital filters and multi-rate filter banks, as well a range of topics that span from sensor array processing, deep convolutional neural network based approaches to the application of chaos theory, and far more. Topics covered by scope include, but are not limited to: advances in single and multi-dimensional filter design and implementation linear and nonlinear, fixed and adaptive digital filters and multirate filter banks statistical signal processing techniques and analysis classical, parametric and higher order spectral analysis signal transformation and compression techniques, including time-frequency analysis system modelling and adaptive identification techniques machine learning based approaches to signal processing Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques theory and application of blind and semi-blind signal separation techniques signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals direction-finding and beamforming techniques for audio and electromagnetic signals analysis techniques for biomedical signals baseband signal processing techniques for transmission and reception of communication signals signal processing techniques for data hiding and audio watermarking sparse signal processing and compressive sensing Special Issue Call for Papers: Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf
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