Детальная информация

The main task of this article is to propose a method for estimating noise levels, then test the noise estimation algorithm, and draw relevant conclusions, such as the uniformity of noise distribution, comparison of noise levels in different systems, and the effectiveness of the proposed estimation method. In terms of experimental data processing, MATLAB software was used, including creating fiber channel diagrams, calculating noise levels through a combination of silent segment analysis and median frequency domain analysis. By amplifying the waveform, the characteristics of the signal can be more accurately analyzed, including distinguishing between noise levels and real signals, analyzing whether the waveform presents a stable background, and whether there are obvious signal reflection peaks or noise fluctuations. Through this method, optical time-domain reflectometer equipment or related data processing software can be used to visualize the signal waveform of the silent section, helping to observe signal characteristics more intuitively, analyze noise levels, analyze whether the waveform presents a stable background, whether there are obvious signal reflection peaks or noise fluctuations, and distinguish between real signals and noise components. This can help accurately estimate noise levels and perform corresponding data analysis and processing. It can improve the accuracy and reliability of noise level estimation. By utilizing advanced mathematical formulas, calibration techniques, and data analysis based on MATLAB, researchers can accurately estimate the noise level of OTDR signals. It can better understand the status of fiber optic lines, which not only helps to improve the reliability and performance of fiber optic communication systems, but also improves network maintenance efficiency, optimizes resource allocation, and takes corresponding maintenance and optimization measures to reduce operating costs, ultimately ensuring high-quality communication services.

• АДАНИЕ
• по выполнению выпускной квалификационной работы
• ABSTRACT
• CONTENTS
• INTRODUCTION
• CHAPTER 1. OTDR NOISE LEVEL ESTIMATION
• 1.2 Distributed fiber optic sensors
• 1.3 Theoretical basis of distributed fiber optic s
• 1.3.1Distributed Acoustic Sensing
• 1.3.2 Rayleigh backscattering principle in optical
• 1.3.3 Principle of optical time-domain reflection
• 1.4 System noise analysis
• 1.4.1 Environmental noise in system signals
• 1.4.2 Circuit noise in system signals
• Where,I0 represents the reverse saturation curr
• In the case of low input optical power,dark curren
• Based on the above,the approximate current noise o
• 1.4.3 Quantization Noise Model
• DAS is a technique that utilizes the phase informa
• At the receiving end, when using A/D to convert an
• According to equation (3.11), the variation of pha
• A series of simulations and experiments were condu
• Analyzing the background noise changes of signal P
• 1.5 Summary
• Firstly, the difference between ordinary sensors a
• CHAPTER 2. EVALUATING NOISE OF PHASE-SENSITIVE OTD
• Phase-sensitive OTDR(Φ-OTDR) is a distributed fibe
• The basic structure of O-OTDR is shown in Fig.2.1.
• When the optical fiber is disturbed by an external
• The essence of heterodyne detection is the interfe
• In the Φ -OTDR system, as the pulse light is trans
• Among them, c is the speed of light in vacuum, and
• Φ -OTDR utilizes narrow pulse width laser pulses f
• 2.2.1 Intelligent Distributed Acoustic Sensor
• The selection of intelligent distributed acoustic
• The interrogator used in the project is iDAS from
• 2.2.2 Double Tube Fibre Optic Cable
• Double tube optical cable, as a specially designed
• Overall, the main reasons for choosing dual tube o
• 2.3 Experimental Plan
• My main task is to propose ways to estimate noise
• In this article, I choose to use a combination of
• 2.3.1 Silent segment definition
• Silent Segment refers to the part of the OTDR sign
• No major events: No prominent reflection or refrac
• Uniform segment: The signal is relatively stable i
• Significant background noise: The silent segment m
• 2.3.2 Selection criteria for silent segment
• The selection of silent segments is crucial for th
• Signal stationarity: Select the part of the signal
• Appropriate length: The length of the segment shou
• No significant event interference: The segment sho
• 2.3.3 Median spectrum and standard deviation
• In the field of signal processing, spectrum analys
• Next, calculate the amplitude of the spectrum, whi
• Through these steps, we can estimate the noise lev
• In the field of signal processing, using median sp
• Secondly, this method has strong robustness. The m
• In addition, the application range of median spect
• CHAPTER 3. DATA PROCESSING AND DATA ANALYSIS
• 3.1 First set of data analysis
• From the OTDR noise level estimates and the noise
• Fig.3.6 shows the placement of the standard deviat
• 3.2 Second set of data analysis
• From the OTDR noise level estimates and the noise
• Fig.3.12 shows the placement of the standard devia
• 3.3 Third set of data analysis
• From the OTDR noise level estimates and the noise
• Fig.3.18 shows the placement of the standard devia
• CONCLUSION
• RENFERENCES