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Demodulation method for GaAs optical fiber temperature sensing based on reference filter and cross-correlation algorithm
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摘要
本文提出了一种基于参考滤波器及互相关算法的新型砷化镓光纤温度解调方法。该方法利用二次高斯滤波方法实现数据平滑预处理，采用长波通滤波(LPF)波形作为参考波形的改进型互相关算法，实现砷化镓光纤温度的解调。基于获取的互相关运算相关系数结果，采用多次多项式拟合，进一步提高互相关算法解调精度。在−30 ℃至250 ℃的测温范围内，该方法的波长解调误差可达到±0.0016 nm，平均温度解调误差为±0.388 ℃。相较于现有的归一化光强解调法，采用LPF波形作为参考波形的互相关算法在抗噪性能上实现了2.64倍的提升，且较之于未使用LPF参考波形的互相关算法提升了2.08倍。
Abstract
This paper presents a new demodulation approach for optical fiber temperature sensors based on GaAs, leveraging reference filtering and a cross-correlation algorithm. It preprocesses the data through double Gaussian filtering for smoothing and implements an enhanced cross-correlation algorithm adopting a long-pass filter (LPF) waveform as the reference signal to demodulate the GaAs optical fiber temperature sensor. Using the correlated data from cross-correlation operations, it applies a multiple polynomial fitting strategy to further augment the precision of the cross-correlation algorithm’s demodulation. Across a temperature sensing range of −30 to 250 ℃, the wavelength demodulation error of this method can reach ±0.0016 nm, and the temperature demodulation accuracy is ±0.388 ℃. Relative to the prevailing normalized optical intensity demodulation method, the cross-correlation algorithm employing an LPF waveform as the reference demonstrates a 2.64-fold increase in noise immunity and a 2.08-fold improvement over cross-correlation algorithms without the LPF reference waveform.
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Key words:
- GaAs /
- Gaussian filtering /
- cross-correlation algorithm
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Overview
Overview: GaAs, as a unique semiconductor material, is widely used in the field of optical communication and the production of various sensors. The temperature characteristics of GaAs material play an important role, but the existing demodulation technologies for the temperature response characteristics of GaAs have problems such as low noise resistance, low precision, and low accuracy. Therefore, a high precision and noise immunity demodulation method for the temperature response of GaAs crystals is needed. This paper proposes a new demodulation approach for optical fiber temperature sensors based on GaAs, leveraging the reference filtering and a cross-correlation algorithm. The algorithm mainly consists of a double Gaussian filtering algorithm for filtering and smoothing the original collected waveform, a cross-correlation algorithm using a low-pass filter (LPF) waveform as the reference waveform, and a multi-quadratic polynomial fitting algorithm for improving the demodulation precision and accuracy. The double Gaussian filtering of this algorithm can reduce the impact of noise during data collection, enhancing the algorithm's noise resistance. Compared with cross-correlation algorithms without the LPF reference waveform, this algorithm uses the LPF waveform collected by the same experimental data acquisition system as the reference waveform, solving the problem of low noise resistance when using the collected waveform as the reference and the inability of virtual waveforms to reflect the error impact of unstable factors in the collection system, such as the light source and spectrometer. At the same time, the use of a multi-quadratic polynomial fitting method ensures the accuracy and reliability of the maximum cross-correlation coefficient acquisition. Compared with the existing GaAs temperature response demodulation technologies, the noise resistance of this algorithm can be improved by up to 2.64 times. Meanwhile, the wavelength demodulation error of this method can reach ±0.0016 nm, and the temperature demodulation accuracy is ±0.388 ℃ with a temperature sensing range of −30 to 250 ℃, meeting the high-precision demodulation requirements in various application scenarios.
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图 1 砷化镓光纤温度解调方法原理图
Figure 1. Schematic diagram of demodulation method for GaAs fiber optic temperature sensing
图 2 砷化镓温度响应数据采集装置图
Figure 2. GaAs temperature response data acquisition device diagram
图 3 不同温度下的砷化镓晶体波长漂移曲线(缩减版)。(a)原始光谱； (b)滤波归一化光谱
Figure 3. Wavelength shift curve of GaAs crystals at different temperatures (scale-down). (a) Original spectrum; (b) Filtered normalized spectrum
图 4 (a)滤波归一化处理后的LPF波形；(b) 60 ℃下互相关漂移量与相关系数多次多项式拟合曲线
Figure 4. (a) The LPF waveform after filtering and normalization processing; (b) Multi-quadratic polynomial fitting curve of cross-correlation shift number and correlation coefficient at 60 ℃
图 6 添加信噪比为20 dB的高斯噪声下的反射光谱
Figure 6. Reflection spectrum with Gaussian noise at SNR of 20 dB
图 7 信噪比为3 dB至54 dB的高斯噪声下不同解调方法的抗噪性能对比
Figure 7. Comparison of noise resistance performance of different demodulation methods under Gaussian noise with SNR ratio of 3 to 54 dB
图 8 三个通道中LPF-C解调方法相较于NI与TC的抗噪效能提升倍率
Figure 8. Enhancement ratio of LPF-C's noise resistance over NI and TC, in three channels
表 1 三个通道下归一化光强法(NI)、传统互相关算法(TC)与LPF参考波形互相关算法(LPF-C)的RMSE指标参数和平均温度解调误差
Table 1. Parameters of the RMSE indicator of normalized intensity method (NI), traditional cross-correlation algorithm (TC), and LPF reference waveform cross-correlation algorithm (LPF-C) in three channels, and average temperature demodulation error (ATDE)
Channel CH-1 CH-2 CH-3 TDA / ℃ NI 0.226123 0.208136 0.306429 0.469 TC 0.162197 0.17823 0.241403 0.408 LPF-C 0.107920 0.100972 0.115995 0.388 
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表 2 不同解调方法的性能对比
Table 2. Performance comparison of different demodulation methods
Ref Year Demodulation method Temperature sensitivity Temperature
demodulation accuracyWavelength
demodulation accuracyResolution [22] 2018 Wavelength demodulation 30 pm/℃ (for test) ± 0.2 ℃ 3 pm — [23] 2018 Fast cross correlation demodulation
algorithm based on dichotomy0.0205 ℃/μm 0.15 ℃ — 0.001 ℃ 2019 Optical cross correlation 14.076 pixel/℃ — — 0.005 ℃ /0.6 nm [24] 2023 Deep belief networks (DBNs) with
ensemble learning— 0.3% F.S. TDE (temperature demodulation
error)=0.98 ℃This paper LPF-cross correlation 0.3 nm/℃ TDE=0.388 ℃ 1.6 pm —
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