{"title":"Weak Coherent Light Interference Heterodyne Detection Based on Time-Domain Signal Analysis","authors":"Hui Shen, Yousen Li","doi":"10.1049/sil2/9918739","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2025 1","pages":""},"PeriodicalIF":1.4000,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sil2/9918739","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/sil2/9918739","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
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.
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