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Fiber Optics and Optical Communications|325 Article(s)
UAV UV Information Collection Method Based on Deep Reinforcement Learning
Taifei ZHAO, Jiahao GUO, Yu XIN, and Lu WANG
In recent years, Unmanned Aerial Vehicles (UAVs) have been widely used across various fields due to their high mobility, flexibility, and cost-effectiveness. In the civilian fields, UAVs are utilised for activities such as agriculture, environmental monitoring, and search and rescue operations. Conversely, in the military fields, UAVs are employed for a range of purposes, including surveillance, reconnaissance, precision strikes, and target guidance. Ground-based battlefield reconnaissance sensor systems currently deployed by military forces include battlefield reconnaissance radars, magnetic sensors, infrared sensors, vibration sensors, acoustic sensors, and pressure sensors. UAVs are increasingly playing a crucial role in information collection for these ground sensors. Traditional communication methods for information collection typically rely on radio communication, which can be severely disrupted or rendered unusable in environments with electromagnetic shielding or interference. Solar-blind ultraviolet (UV) light, operating within the 200 nm-280 nm wavelength range, offers virtually no background noise in low-altitude airspace and provides all-weather, non-line-of-sight communication capabilities. This makes it an ideal communication method in electromagnetically challenged environments due to its excellent environmental adaptability, high confidentiality, and strong resistance to electromagnetic interference. Compared to line-of-sight UV communication, non-line-of-sight communication does not require precise alignment between the transmitter and receiver and offers greater flexibility in receiver positioning, making it more suitable for collecting information from ground sensors. However, traditional algorithms often face limitations in handling complex information collection tasks, particularly in terms of computational resources, adaptability, and real-time performance. Deep reinforcement learning (DRL) algorithms, as an emerging intelligent decision-making method, enable UAVs to autonomously complete tasks by learning and experimenting within the environment. This makes DRL an ideal approach for autonomous UAV navigation and data collection tasks.This paper addresses the challenge of UAV information collection in the presence of electromagnetic interference by employing an adaptive elevation angle UV non-line-of-sight communication method and utilizing DRL algorithms to tackle the information collection task. First, a UAV mobility model is established, followed by the proposal of a UV non-line-of-sight air-to-ground communication model with variable transmission and reception angles. Detailed modeling of the UAV's energy consumption is then carried out, considering flight energy consumption, the energy consumption of the electro-optical pod, and communication energy consumption. Subsequently, an information collection model is established. This integrated model balances task execution time, energy consumption, and communication quality during the information collection process. Given that the optimization problem is NP-hard, traditional polynomial optimization algorithms are inadequate for solving it. Therefore, this problem is formulated as a Markov decision process. To enable the UAV to make better decisions regarding flight direction, speed, and UV transmission and reception angles, a reward function tailored to the information collection task is designed. This reward function comprehensively considers time, energy, communication path loss, and the UAV's return to base. The Double Deep Q-Network (DDQN) algorithm, which separates action selection from evaluation, still faces overestimation issues in high-dimensional state and action spaces. This paper proposes that in the information collection scenario, the UAV must consider multiple directions and speeds of movement while adaptively adjusting the UV communication angles during the collection process. Compared to previous discrete environments with smaller action spaces, this scenario requires a larger action space. To better adapt to this scenario, improvements such as dual target networks, prioritized experience replay, and entropy regularization are incorporated into the classical DDQN algorithm, enhancing its adaptability and stability.To verify the effectiveness of the improved DDQN algorithm and explore the impact of different UV parameters, sensor quantities, and UAV flight altitudes on information collection time and energy consumption, comparative simulations with the classical DDQN algorithm are conducted. The proposed adaptive elevation angle DDQN algorithm effectively completes the information collection task, demonstrating at least a 13% improvement in time efficiency and a 14% reduction in energy consumption across multiple scenarios compared to the classical DDQN algorithm. In recent years, Unmanned Aerial Vehicles (UAVs) have been widely used across various fields due to their high mobility, flexibility, and cost-effectiveness. In the civilian fields, UAVs are utilised for activities such as agriculture, environmental monitoring, and search and rescue operations. Conversely, in the military fields, UAVs are employed for a range of purposes, including surveillance, reconnaissance, precision strikes, and target guidance. Ground-based battlefield reconnaissance sensor systems currently deployed by military forces include battlefield reconnaissance radars, magnetic sensors, infrared sensors, vibration sensors, acoustic sensors, and pressure sensors. UAVs are increasingly playing a crucial role in information collection for these ground sensors. Traditional communication methods for information collection typically rely on radio communication, which can be severely disrupted or rendered unusable in environments with electromagnetic shielding or interference. Solar-blind ultraviolet (UV) light, operating within the 200 nm-280 nm wavelength range, offers virtually no background noise in low-altitude airspace and provides all-weather, non-line-of-sight communication capabilities. This makes it an ideal communication method in electromagnetically challenged environments due to its excellent environmental adaptability, high confidentiality, and strong resistance to electromagnetic interference. Compared to line-of-sight UV communication, non-line-of-sight communication does not require precise alignment between the transmitter and receiver and offers greater flexibility in receiver positioning, making it more suitable for collecting information from ground sensors. However, traditional algorithms often face limitations in handling complex information collection tasks, particularly in terms of computational resources, adaptability, and real-time performance. Deep reinforcement learning (DRL) algorithms, as an emerging intelligent decision-making method, enable UAVs to autonomously complete tasks by learning and experimenting within the environment. This makes DRL an ideal approach for autonomous UAV navigation and data collection tasks.This paper addresses the challenge of UAV information collection in the presence of electromagnetic interference by employing an adaptive elevation angle UV non-line-of-sight communication method and utilizing DRL algorithms to tackle the information collection task. First, a UAV mobility model is established, followed by the proposal of a UV non-line-of-sight air-to-ground communication model with variable transmission and reception angles. Detailed modeling of the UAV's energy consumption is then carried out, considering flight energy consumption, the energy consumption of the electro-optical pod, and communication energy consumption. Subsequently, an information collection model is established. This integrated model balances task execution time, energy consumption, and communication quality during the information collection process. Given that the optimization problem is NP-hard, traditional polynomial optimization algorithms are inadequate for solving it. Therefore, this problem is formulated as a Markov decision process. To enable the UAV to make better decisions regarding flight direction, speed, and UV transmission and reception angles, a reward function tailored to the information collection task is designed. This reward function comprehensively considers time, energy, communication path loss, and the UAV's return to base. The Double Deep Q-Network (DDQN) algorithm, which separates action selection from evaluation, still faces overestimation issues in high-dimensional state and action spaces. This paper proposes that in the information collection scenario, the UAV must consider multiple directions and speeds of movement while adaptively adjusting the UV communication angles during the collection process. Compared to previous discrete environments with smaller action spaces, this scenario requires a larger action space. To better adapt to this scenario, improvements such as dual target networks, prioritized experience replay, and entropy regularization are incorporated into the classical DDQN algorithm, enhancing its adaptability and stability.To verify the effectiveness of the improved DDQN algorithm and explore the impact of different UV parameters, sensor quantities, and UAV flight altitudes on information collection time and energy consumption, comparative simulations with the classical DDQN algorithm are conducted. The proposed adaptive elevation angle DDQN algorithm effectively completes the information collection task, demonstrating at least a 13% improvement in time efficiency and a 14% reduction in energy consumption across multiple scenarios compared to the classical DDQN algorithm.
Acta Photonica Sinica
- Publication Date: Jan. 25, 2025
- Vol. 54, Issue 1, 0106003 (2025)
Distributed Fiber Optic Acoustic Sensing System Based on Fading Mask Autoencoder and Application in Water Navigation Security Events Identification
Miao YU, Yutong HE, Tianying CHANG, Hongliang CUI... and Qingguo GAO|Show fewer author(s)
In recent years, intelligent navigation security is a hot spot in the field of water safety protection. Distributed optical fiber acoustic sensing technology based on phase sensitive optical time domain reflectometer can realize distributed monitoring of multi-point disturbance along optical fiber. However, due to the complex water environment and the fading of system signals, it is difficult to identify the disturbance signals stably and effectively. The main noise sources of distributed optical fiber acoustic sensing system are interference fading and polarization fading. Both of these phenomena greatly weaken the signal at the fading point, resulting in signal distortion. Under water, the cable is less coupled with the environment, and is more susceptible to the influence of water waves, currents and other factors. Therefore, timely and effective elimination of fading interference plays an important role in the identification of water security events. Combining fading mask, attention mechanism and self-supervised learning, this paper proposes a distributed optical fiber acoustic sensing system based on fading mask autoencoder for ship security event recognition in waters. This method is aimed at the ship event signal in the water area, and generates a basically noise-free signal by shielding fading noise into the deep learning model, so that the model can learn directly and greatly reduce the influence of fading noise on signal recognition. Mask autoencoder is an extensible self-supervised learner. It combines the attention mechanism in the form of a mask to achieve high-precision training with the simplest coding-decoding model structure, while it can also transfer learning only through the model weights of the encoder. On this basis, the fading mask autoencoder method is more helpful for the distributed optical fiber acoustic sensing system to achieve effective event recognition. Firstly, the amplitude signal of distributed optical fiber acoustic sensing is analyzed to determine the fading position. The model is then pre-trained using the upstream task of the fading mask autoencoder. The basic characteristics of distributed optical fiber acoustic sensing signal are learned by means of random mask. Finally, the downstream task of fading mask autoencoder completes the intelligent event recognition training by fading position mask. In this paper, four kinds of ship security events collected at the water test site are used as classification data, and the fading mask autoencoder is compared with the mask autoencoder and three related models. The results show that the average training accuracy of the fading mask autoencoder is 98.34%, which is 4.9% higher than that of the mask autoencoder. The average training loss was 0.1094, which was 0.095 less than that of the mask autoencoder. The average test accuracy was 93.01%, which was 6.45% higher than that of the mask autoencoder. Compared with the other three models, the fading mask autoencoder has higher training accuracy, lower Loss and faster convergence speed. Its average performance index is about 4.88%-7.62% higher than other models. Therefore, the fading mask autoencoder model based on the improved mask strategy can extract useful information from the signal more efficiently and accurately for training, and has better stability and generalization, which is suitable for the identification of navigation security events in waters. In recent years, intelligent navigation security is a hot spot in the field of water safety protection. Distributed optical fiber acoustic sensing technology based on phase sensitive optical time domain reflectometer can realize distributed monitoring of multi-point disturbance along optical fiber. However, due to the complex water environment and the fading of system signals, it is difficult to identify the disturbance signals stably and effectively. The main noise sources of distributed optical fiber acoustic sensing system are interference fading and polarization fading. Both of these phenomena greatly weaken the signal at the fading point, resulting in signal distortion. Under water, the cable is less coupled with the environment, and is more susceptible to the influence of water waves, currents and other factors. Therefore, timely and effective elimination of fading interference plays an important role in the identification of water security events. Combining fading mask, attention mechanism and self-supervised learning, this paper proposes a distributed optical fiber acoustic sensing system based on fading mask autoencoder for ship security event recognition in waters. This method is aimed at the ship event signal in the water area, and generates a basically noise-free signal by shielding fading noise into the deep learning model, so that the model can learn directly and greatly reduce the influence of fading noise on signal recognition. Mask autoencoder is an extensible self-supervised learner. It combines the attention mechanism in the form of a mask to achieve high-precision training with the simplest coding-decoding model structure, while it can also transfer learning only through the model weights of the encoder. On this basis, the fading mask autoencoder method is more helpful for the distributed optical fiber acoustic sensing system to achieve effective event recognition. Firstly, the amplitude signal of distributed optical fiber acoustic sensing is analyzed to determine the fading position. The model is then pre-trained using the upstream task of the fading mask autoencoder. The basic characteristics of distributed optical fiber acoustic sensing signal are learned by means of random mask. Finally, the downstream task of fading mask autoencoder completes the intelligent event recognition training by fading position mask. In this paper, four kinds of ship security events collected at the water test site are used as classification data, and the fading mask autoencoder is compared with the mask autoencoder and three related models. The results show that the average training accuracy of the fading mask autoencoder is 98.34%, which is 4.9% higher than that of the mask autoencoder. The average training loss was 0.1094, which was 0.095 less than that of the mask autoencoder. The average test accuracy was 93.01%, which was 6.45% higher than that of the mask autoencoder. Compared with the other three models, the fading mask autoencoder has higher training accuracy, lower Loss and faster convergence speed. Its average performance index is about 4.88%-7.62% higher than other models. Therefore, the fading mask autoencoder model based on the improved mask strategy can extract useful information from the signal more efficiently and accurately for training, and has better stability and generalization, which is suitable for the identification of navigation security events in waters.
Acta Photonica Sinica
- Publication Date: Jan. 25, 2025
- Vol. 54, Issue 1, 0106002 (2025)
FBG Wavelength Demodulation Method Based on Support Vector Regression Optimized by Sparrow Search Algorithm
Yinggang LIU, Fei LI, Yubo YUAN, Rui LI... and Xinyi XU|Show fewer author(s)
Fiber Bragg grating (FBG) wavelength demodulation technique based on tunable Fabry-Perot (F-P) filters (FFP-TF) results in a degradation of the accuracy of the demodulation system due to the hysteresis and temperature drift characteristics of piezoelectric ceramics (PZT) inside the FFP-TF. Artificial intelligence machine learning algorithms can reduce the demodulation error of the system without increasing the complexity of the system. Therefore, this article proposes an FBG wavelength demodulation method based on Support Vector Regression (SVR) optimized by Sparrow Search Algorithm (SSA). The main purpose is to establish a nonlinear fitting relationship between F-P tuning voltage and transmission wavelength, replacing wavelength reference hardware. This method can save system costs, improve system demodulation accuracy, and contribute to the integrated development of demodulation systems. First, the reference grating, driving voltage, tuning time and F-P surface temperature are taken as the input features of the model, and the F-P transmission wavelength is taken as the output feature, and the F-P transmission wavelength compensation model is established by SVR. The SSA-optimized hyperparameters C and g are then input into the SVR model for training to obtain the target compensation model. Meanwhile, the training method of combining sliding window with SVR is proposed to improve the model's global optimization-seeking ability by updating the sliding window's size and sliding speed, which avoids the problem of the model's deterioration in generalization ability over time. After the model training, the correlation coefficient (R2), Mean Square Error (MSE), and Root Mean Square Error (RMSE) are introduced to evaluate the model and obtain the optimal compensation model. Finally, the FBG wavelength demodulation system based on SSA-SVR is built in Labview and the optimal training model is called. Then the real-time compensation ability of the model is checked by the stability experiment and the cooling experiment. The law of the wavelength compensation error in the frequency range of 0.5~2.5 Hz driving is explored experimentally, and the compensation ability of the SSA-SVR model is compared with PSO-SVR, LSSVR, and KRR models. The experimental results show that in the frequency range of 0.5~2.5 Hz, the error between the target value output and the real value of the SSA-SVR algorithm model decreases with the decrease of the driving frequency, which indicates that the demodulation system is more accurate under the low-frequency driving. The fitting coefficient R2 of the SSA-SVR algorithm reaches 0.999 99 in both training and test data, which is an improvement of 0.001 compared to the KRR algorithm, and the wavelength demodulation error is reduced by more than ten times. At 2.5 Hz driving frequency, the Mean Absolute Error (MAE) of SSA model is 38.689 pm, which is reduced by 28.078 pm compared to the PSO algorithm error, similarly, at 0.5 Hz driving frequency, the SSA model has a MAE of 19.83 pm, which is reduced by 48.8% compared to the PSO optimization algorithm error of 39.68 pm. In addition, the SSA model has the smallest error in all of the different drive frequency tests, showing better generalization performance, while the LSSVR model performs better at high frequencies and shows negative optimization in the low-frequency tests, and the KRR model has the largest error in a wide range of frequencies, indicating that this model has the worst fit. In the stability experiment at 25 ℃, the fluctuation of the demodulation value based on the SSA-SVR model is kept within 15 pm, and the average absolute error between the demodulation value and that of the sm125 demodulator produced by MOI company in the U.S.A. is 7.28 pm, whereas the stability of the traditional polynomial demodulation method of the reference grating is poorer, and the demodulation error of the SSA is reduced by 88.5% compared with it. For the cooling experiments from 40 to 90 ℃, the detuned MAE of the SVR compensated model is 7.44 pm, which is 27.6% lower relative to the 5.39 pm of the polynomial fitting method. The experiment proved through error analysis that this method is superior to the reference grating polynomial demodulation method in terms of real-time demodulation and stability, and the error between it and the sm125 demodulator remains within a small range. Compared with the traditional method, this method models the wavelength drift during the natural temperature change process of FFP-TF, realizes the nonlinear fitting between the F-P driving voltage and the transmitted wavelength over a wide range, and verifies that the fiber grating demodulation system can effectively reduce the grating wavelength demodulation error without the help of hardware reference. Fiber Bragg grating (FBG) wavelength demodulation technique based on tunable Fabry-Perot (F-P) filters (FFP-TF) results in a degradation of the accuracy of the demodulation system due to the hysteresis and temperature drift characteristics of piezoelectric ceramics (PZT) inside the FFP-TF. Artificial intelligence machine learning algorithms can reduce the demodulation error of the system without increasing the complexity of the system. Therefore, this article proposes an FBG wavelength demodulation method based on Support Vector Regression (SVR) optimized by Sparrow Search Algorithm (SSA). The main purpose is to establish a nonlinear fitting relationship between F-P tuning voltage and transmission wavelength, replacing wavelength reference hardware. This method can save system costs, improve system demodulation accuracy, and contribute to the integrated development of demodulation systems. First, the reference grating, driving voltage, tuning time and F-P surface temperature are taken as the input features of the model, and the F-P transmission wavelength is taken as the output feature, and the F-P transmission wavelength compensation model is established by SVR. The SSA-optimized hyperparameters C and g are then input into the SVR model for training to obtain the target compensation model. Meanwhile, the training method of combining sliding window with SVR is proposed to improve the model's global optimization-seeking ability by updating the sliding window's size and sliding speed, which avoids the problem of the model's deterioration in generalization ability over time. After the model training, the correlation coefficient (R2), Mean Square Error (MSE), and Root Mean Square Error (RMSE) are introduced to evaluate the model and obtain the optimal compensation model. Finally, the FBG wavelength demodulation system based on SSA-SVR is built in Labview and the optimal training model is called. Then the real-time compensation ability of the model is checked by the stability experiment and the cooling experiment. The law of the wavelength compensation error in the frequency range of 0.5~2.5 Hz driving is explored experimentally, and the compensation ability of the SSA-SVR model is compared with PSO-SVR, LSSVR, and KRR models. The experimental results show that in the frequency range of 0.5~2.5 Hz, the error between the target value output and the real value of the SSA-SVR algorithm model decreases with the decrease of the driving frequency, which indicates that the demodulation system is more accurate under the low-frequency driving. The fitting coefficient R2 of the SSA-SVR algorithm reaches 0.999 99 in both training and test data, which is an improvement of 0.001 compared to the KRR algorithm, and the wavelength demodulation error is reduced by more than ten times. At 2.5 Hz driving frequency, the Mean Absolute Error (MAE) of SSA model is 38.689 pm, which is reduced by 28.078 pm compared to the PSO algorithm error, similarly, at 0.5 Hz driving frequency, the SSA model has a MAE of 19.83 pm, which is reduced by 48.8% compared to the PSO optimization algorithm error of 39.68 pm. In addition, the SSA model has the smallest error in all of the different drive frequency tests, showing better generalization performance, while the LSSVR model performs better at high frequencies and shows negative optimization in the low-frequency tests, and the KRR model has the largest error in a wide range of frequencies, indicating that this model has the worst fit. In the stability experiment at 25 ℃, the fluctuation of the demodulation value based on the SSA-SVR model is kept within 15 pm, and the average absolute error between the demodulation value and that of the sm125 demodulator produced by MOI company in the U.S.A. is 7.28 pm, whereas the stability of the traditional polynomial demodulation method of the reference grating is poorer, and the demodulation error of the SSA is reduced by 88.5% compared with it. For the cooling experiments from 40 to 90 ℃, the detuned MAE of the SVR compensated model is 7.44 pm, which is 27.6% lower relative to the 5.39 pm of the polynomial fitting method. The experiment proved through error analysis that this method is superior to the reference grating polynomial demodulation method in terms of real-time demodulation and stability, and the error between it and the sm125 demodulator remains within a small range. Compared with the traditional method, this method models the wavelength drift during the natural temperature change process of FFP-TF, realizes the nonlinear fitting between the F-P driving voltage and the transmitted wavelength over a wide range, and verifies that the fiber grating demodulation system can effectively reduce the grating wavelength demodulation error without the help of hardware reference.
Acta Photonica Sinica
- Publication Date: Jan. 25, 2025
- Vol. 54, Issue 1, 0106001 (2025)
Research on High-precision Extraction Method of Intrinsic Signal in Optical Current Sensing with Debouncing Kalman Filter
Yilun LIU, Penghua XUAN, Yansong LI, Yuanchang RAN, and Qiwei WANG
The intrinsic signal stability is an important performance parameter for long-term stable operation and high-precision measurement in optical current sensing. If the intrinsic signal exhibits fluctuations, drift, or jitter, it can lead to cumulative measurement errors and increased instability in the system. This can result in incorrect current measurement values, misjudgment, and misleading system operation status assessment, thereby affecting the stability and safety of the system. Therefore, ensuring the stability of the intrinsic signal is crucial for maintaining long-term stability in optical current sensing systems.Jitter in the intrinsic signal has a direct negative impact on the measurement accuracy of optical current sensing systems. Jitter introduces fluctuations in the instantaneous signal values, leading to instability and inaccuracy in the measurement results. Particularly in applications requiring high-precision current measurements, the presence of intrinsic signal jitter introduces additional errors, reducing the measurement accuracy and reliability of the system. Currently, conventional methods such as dual-path techniques can not eliminate jitter in the intrinsic signal effectively. Despite the use of dual-path techniques, the intrinsic signal is still influenced by factors such as optical components, circuit noise, and environmental interference, and the jitter can not be completely eliminated. To address the issue of intrinsic signal jitter in optical current sensing systems and improve measurement accuracy, a debouncing Kalman-based method for high-precision extraction of the intrinsic signal is proposed. This method involves a detailed analysis of the noise characteristics and optical path structure of the optical current sensing system, followed by the establishment of a mathematical model for the intrinsic signal. Subsequently, a debouncing function is introduced to modify the Kalman gain K, resulting in a Debouncing Kalman (DBKalgorithm. The debouncing Kalman algorithm aims to address the severe estimation jitter in the state estimates caused by the initial state dependence and measurement process uncertainty sensitivity of the standard Kalman gain K.In this method, the debouncing Kalman filtering algorithm utilizes the debouncing function to modify the Kalman gain K, thereby providing denoising processing for the intrinsic signal. The introduction of the debouncing function allows the Kalman filtering algorithm to better adapt to the jitter characteristics of the intrinsic signal, reducing the impact of jitter on state estimation. Compared to traditional Kalman filtering algorithms, the debouncing Kalman filtering algorithm exhibits greater stability in the state estimation process and can effectively extract high-precision estimates of the intrinsic signal. Additionally, a recursive estimation of the noise variance is introduced to ensure real-time correction of the noise variance during the filtering process.The debouncing Kalman algorithm was validated and compared with the standard Kalman algorithm through simulations in MATLAB. The simulation results show that, under the same set of parameters, the relative error of the standard Kalman algorithm reaches approximately 11% after convergence, while the debouncing Kalman algorithm achievs a relative error of approximately 2% after convergence. This validates the feasibility of the proposed algorithm. Furthermore, the stability of the algorithm was derived and verified using the Lyapunov stability analysis method. Finally, an optical current sensing experimental platform was constructed, and the proposed algorithm was implemented in parallel on the LabVIEW FPGA hardware platform. The experimental results demonstrate that the amplitude error of the filtered intrinsic component is within 2%. This verifies the real-time performance of the algorithm and its ability to meet practical engineering requirements. The successful construction of the experimental platform and the parallel implementation of the algorithm on the hardware platform further demonstrate the real-time capability and feasibility of the proposed algorithm. It provides strong support for practical applications in the field of optical current sensing and offers a high-precision and stable solution to current measurement problems in engineering practice. The intrinsic signal stability is an important performance parameter for long-term stable operation and high-precision measurement in optical current sensing. If the intrinsic signal exhibits fluctuations, drift, or jitter, it can lead to cumulative measurement errors and increased instability in the system. This can result in incorrect current measurement values, misjudgment, and misleading system operation status assessment, thereby affecting the stability and safety of the system. Therefore, ensuring the stability of the intrinsic signal is crucial for maintaining long-term stability in optical current sensing systems.Jitter in the intrinsic signal has a direct negative impact on the measurement accuracy of optical current sensing systems. Jitter introduces fluctuations in the instantaneous signal values, leading to instability and inaccuracy in the measurement results. Particularly in applications requiring high-precision current measurements, the presence of intrinsic signal jitter introduces additional errors, reducing the measurement accuracy and reliability of the system. Currently, conventional methods such as dual-path techniques can not eliminate jitter in the intrinsic signal effectively. Despite the use of dual-path techniques, the intrinsic signal is still influenced by factors such as optical components, circuit noise, and environmental interference, and the jitter can not be completely eliminated. To address the issue of intrinsic signal jitter in optical current sensing systems and improve measurement accuracy, a debouncing Kalman-based method for high-precision extraction of the intrinsic signal is proposed. This method involves a detailed analysis of the noise characteristics and optical path structure of the optical current sensing system, followed by the establishment of a mathematical model for the intrinsic signal. Subsequently, a debouncing function is introduced to modify the Kalman gain K, resulting in a Debouncing Kalman (DBKalgorithm. The debouncing Kalman algorithm aims to address the severe estimation jitter in the state estimates caused by the initial state dependence and measurement process uncertainty sensitivity of the standard Kalman gain K.In this method, the debouncing Kalman filtering algorithm utilizes the debouncing function to modify the Kalman gain K, thereby providing denoising processing for the intrinsic signal. The introduction of the debouncing function allows the Kalman filtering algorithm to better adapt to the jitter characteristics of the intrinsic signal, reducing the impact of jitter on state estimation. Compared to traditional Kalman filtering algorithms, the debouncing Kalman filtering algorithm exhibits greater stability in the state estimation process and can effectively extract high-precision estimates of the intrinsic signal. Additionally, a recursive estimation of the noise variance is introduced to ensure real-time correction of the noise variance during the filtering process.The debouncing Kalman algorithm was validated and compared with the standard Kalman algorithm through simulations in MATLAB. The simulation results show that, under the same set of parameters, the relative error of the standard Kalman algorithm reaches approximately 11% after convergence, while the debouncing Kalman algorithm achievs a relative error of approximately 2% after convergence. This validates the feasibility of the proposed algorithm. Furthermore, the stability of the algorithm was derived and verified using the Lyapunov stability analysis method. Finally, an optical current sensing experimental platform was constructed, and the proposed algorithm was implemented in parallel on the LabVIEW FPGA hardware platform. The experimental results demonstrate that the amplitude error of the filtered intrinsic component is within 2%. This verifies the real-time performance of the algorithm and its ability to meet practical engineering requirements. The successful construction of the experimental platform and the parallel implementation of the algorithm on the hardware platform further demonstrate the real-time capability and feasibility of the proposed algorithm. It provides strong support for practical applications in the field of optical current sensing and offers a high-precision and stable solution to current measurement problems in engineering practice.
Acta Photonica Sinica
- Publication Date: Sep. 25, 2024
- Vol. 53, Issue 9, 0906003 (2024)
Birefringence Performance of Side-hole Optical Fiber by Pressure Based on Polarized Light Interference
Peiming WANG, Youlong YU, Yi YU, Bin LIU, and Kexin MIAO
Amid the rapid advancements in optical fiber communications and sensing technologies, polarization-maintaining fibers have increasingly been utilized in fiber sensing systems. As a novel type of polarization-maintaining fiber, Side-Hole Fiber (SHF), with its unique microstructure and superior performance, holds broad application prospects in fields such as communication, sensing, and medical technology. The sensors made from SHF are highly sensitive, capable of monitoring multiple parameters simultaneously, and are easily integrated into the materials being measured. They play an extremely important role in the field of structural health monitoring, thereby attracting widespread attention. In recent years, many researchers have studied and analyzed the structure and birefringence properties of SHF. However, systematic analysis and research on the impact of pressure on the birefringence performance of SHF have not been reported.In this paper, the impact of radial pressure on the birefringence characteristics of SHF was analyzed systematically based on coupled mode theory and the photoelastic effect. To facilitate the experimental component of the study, a mechanical loading apparatus was engineered to apply varying levels of radial pressure on the SHF using different weights. Furthermore, we established an experimental system grounded in the principle of polarization interference, designed specifically to measure the birefringence of SHF under different pressure conditions. The experimental setup comprised a broadband light source, a polarizer, the SHF under test, and a spectrometer. Light from the broadband source, after passing through the polarizer, was transmitted through the SHF. The interference spectrum was subsequently captured by the spectrometer. Birefringence was quantified by analyzing the mean wavelength of troughs and the average interval between adjacent peaks within the interference spectrum.Experimental results indicated that while keeping the pressure magnitude constant, the birefringence values varied according to a cosine function with respect to the direction of application, achieving maximum and minimum values at even and odd multiples of π/2, respectively. When the direction of application was held constant, the birefringence values exhibited a linear relationship with the magnitude of pressure. Specifically, for angles θ within the range (kπ-π/4,kπ+π/4)(where k is an integer), birefringence values increased linearly with pressure. Conversely, for θ in the range (kπ+π/4,kπ+3π/4), birefringence values decreased linearly with pressure. At θ=kπ+π/4 the birefringence values remained essentially unchanged. The correlation coefficient r between the experimental and simulation results was 0.992 2, indicating a high degree of consistency within the permissible error range. Amid the rapid advancements in optical fiber communications and sensing technologies, polarization-maintaining fibers have increasingly been utilized in fiber sensing systems. As a novel type of polarization-maintaining fiber, Side-Hole Fiber (SHF), with its unique microstructure and superior performance, holds broad application prospects in fields such as communication, sensing, and medical technology. The sensors made from SHF are highly sensitive, capable of monitoring multiple parameters simultaneously, and are easily integrated into the materials being measured. They play an extremely important role in the field of structural health monitoring, thereby attracting widespread attention. In recent years, many researchers have studied and analyzed the structure and birefringence properties of SHF. However, systematic analysis and research on the impact of pressure on the birefringence performance of SHF have not been reported.In this paper, the impact of radial pressure on the birefringence characteristics of SHF was analyzed systematically based on coupled mode theory and the photoelastic effect. To facilitate the experimental component of the study, a mechanical loading apparatus was engineered to apply varying levels of radial pressure on the SHF using different weights. Furthermore, we established an experimental system grounded in the principle of polarization interference, designed specifically to measure the birefringence of SHF under different pressure conditions. The experimental setup comprised a broadband light source, a polarizer, the SHF under test, and a spectrometer. Light from the broadband source, after passing through the polarizer, was transmitted through the SHF. The interference spectrum was subsequently captured by the spectrometer. Birefringence was quantified by analyzing the mean wavelength of troughs and the average interval between adjacent peaks within the interference spectrum.Experimental results indicated that while keeping the pressure magnitude constant, the birefringence values varied according to a cosine function with respect to the direction of application, achieving maximum and minimum values at even and odd multiples of π/2, respectively. When the direction of application was held constant, the birefringence values exhibited a linear relationship with the magnitude of pressure. Specifically, for angles θ within the range (kπ-π/4,kπ+π/4)(where k is an integer), birefringence values increased linearly with pressure. Conversely, for θ in the range (kπ+π/4,kπ+3π/4), birefringence values decreased linearly with pressure. At θ=kπ+π/4 the birefringence values remained essentially unchanged. The correlation coefficient r between the experimental and simulation results was 0.992 2, indicating a high degree of consistency within the permissible error range.
Acta Photonica Sinica
- Publication Date: Sep. 25, 2024
- Vol. 53, Issue 9, 0906002 (2024)
High-sensitivity Simultaneous Demodulation of Multi-parameters in Polarization-maintaining Fibers Based on Brillouin Dynamic Gratings
Lijuan ZHAO, Zimeng HUANG, and Zhiniu XU
Distributed Brillouin fiber sensing technology enables continuous spatial measurement of parameters such as temperature and pressure, offering advantages over traditional point sensors in terms of wide range, long distance, and high capacity. Civil structures and large machinery inevitably face lateral pressures due to their own weight and external impacts during construction and use, necessitating reliable and efficient sensors for these forces. Additionally, temperature is a crucial physical parameter that often needs to be measured simultaneously with pressure. The use of Brillouin frequency shift in fiber optic distributed sensing for temperature or pressure is common, but its sensitivity to both parameters simultaneously complicates the measurement of multiple variables at once. Hence, this paper introduces a simultaneous demodulation method for temperature, fast-axis pressure, and slow-axis pressure. The numerical simulation and emulation were performed using the wave optics and solid mechanics modules within the COMSOL Multiphysics finite element analysis software. After setting the boundary conditions, pressure was applied to the photonic crystal fiber, and its deformation under pressure was calculated. The effective refractive index of the fiber was calculated using the wave optics module. By substituting into formulas, the birefringence frequency shift, Brillouin frequency shift, and Brillouin linewidth resulting from deformation were obtained. Demodulation was then employed to acquire the specific values of these three variables. To validate the reliability of the demodulation results, lateral pressure is applied to both the fast and slow axes, while simultaneously altering the temperature. Using the birefringence frequency shift, Brillouin frequency shift, and Brillouin linewidth at 0 MPa and 0 ℃ as reference values, simulations determined the variations in these parameters under different pressures or temperatures. These variations are then substituted into formulas to calculate ?P1', ?P2', and ?T'. By comparing these calculated values with the actual applied values of ?P1, ?P2, and ?T, the corresponding error values can be ascertained. The results indicate that the three parameters can be simultaneously demodulated with demodulation errors within 1 MPa and 1 ℃. The mean errors for fast-axis pressure, slow-axis pressure, and temperature were 0.21 MPa, 0.31 MPa, and 0.30 ℃, respectively, with standard deviations of 0.15 MPa, 0.21 MPa, and 0.21 ℃, respectively. When lateral pressures of 0 to 30 MPa and temperatures of 0 to 100 ℃ were applied, the pressure sensitivity in the fast-axis direction of the photonic crystal fiber was approximately -1.961 GHz/MPa, the pressure sensitivity in the slow-axis direction was about 1.356 GHz/MPa, and its temperature sensitivity was around 0.105 MHz/℃. Compared with the current optimal structure of the photonic crystal fiber, the pressure sensitivity is improved by -957 MHz/MPa. This paper presents a highly sensitive polarization-maintaining photonic crystal fiber that enables simultaneous demodulation of temperature, fast axis pressure, and slow axis pressure. Due to the sensitivity of the polarization-maintaining fiber to temperature, fast axis pressure, and slow axis pressure, this technology can be applied to the detection of high-precision fiber optic gyroscope rings. The proposed sensor and demodulation method offer significant reference value for the distributed monitoring of temperature and pressure in different directions during the construction and use of civil structures and large machinery. Distributed Brillouin fiber sensing technology enables continuous spatial measurement of parameters such as temperature and pressure, offering advantages over traditional point sensors in terms of wide range, long distance, and high capacity. Civil structures and large machinery inevitably face lateral pressures due to their own weight and external impacts during construction and use, necessitating reliable and efficient sensors for these forces. Additionally, temperature is a crucial physical parameter that often needs to be measured simultaneously with pressure. The use of Brillouin frequency shift in fiber optic distributed sensing for temperature or pressure is common, but its sensitivity to both parameters simultaneously complicates the measurement of multiple variables at once. Hence, this paper introduces a simultaneous demodulation method for temperature, fast-axis pressure, and slow-axis pressure. The numerical simulation and emulation were performed using the wave optics and solid mechanics modules within the COMSOL Multiphysics finite element analysis software. After setting the boundary conditions, pressure was applied to the photonic crystal fiber, and its deformation under pressure was calculated. The effective refractive index of the fiber was calculated using the wave optics module. By substituting into formulas, the birefringence frequency shift, Brillouin frequency shift, and Brillouin linewidth resulting from deformation were obtained. Demodulation was then employed to acquire the specific values of these three variables. To validate the reliability of the demodulation results, lateral pressure is applied to both the fast and slow axes, while simultaneously altering the temperature. Using the birefringence frequency shift, Brillouin frequency shift, and Brillouin linewidth at 0 MPa and 0 ℃ as reference values, simulations determined the variations in these parameters under different pressures or temperatures. These variations are then substituted into formulas to calculate ?P1', ?P2', and ?T'. By comparing these calculated values with the actual applied values of ?P1, ?P2, and ?T, the corresponding error values can be ascertained. The results indicate that the three parameters can be simultaneously demodulated with demodulation errors within 1 MPa and 1 ℃. The mean errors for fast-axis pressure, slow-axis pressure, and temperature were 0.21 MPa, 0.31 MPa, and 0.30 ℃, respectively, with standard deviations of 0.15 MPa, 0.21 MPa, and 0.21 ℃, respectively. When lateral pressures of 0 to 30 MPa and temperatures of 0 to 100 ℃ were applied, the pressure sensitivity in the fast-axis direction of the photonic crystal fiber was approximately -1.961 GHz/MPa, the pressure sensitivity in the slow-axis direction was about 1.356 GHz/MPa, and its temperature sensitivity was around 0.105 MHz/℃. Compared with the current optimal structure of the photonic crystal fiber, the pressure sensitivity is improved by -957 MHz/MPa. This paper presents a highly sensitive polarization-maintaining photonic crystal fiber that enables simultaneous demodulation of temperature, fast axis pressure, and slow axis pressure. Due to the sensitivity of the polarization-maintaining fiber to temperature, fast axis pressure, and slow axis pressure, this technology can be applied to the detection of high-precision fiber optic gyroscope rings. The proposed sensor and demodulation method offer significant reference value for the distributed monitoring of temperature and pressure in different directions during the construction and use of civil structures and large machinery.
Acta Photonica Sinica
- Publication Date: Sep. 25, 2024
- Vol. 53, Issue 9, 0906001 (2024)
Fourier Series Based Grating Wavelength Signal Reconstruction and Accurate Vibration Displacement Measurement
Cui ZHANG, Rui LUO, Yinjie ZHANG, Sikai JIA, and Weibing GAN
During operation of a water turbine, the vibration of its mechanical structure may reflect its operating condition. If the peak value of the vibration displacement exceeds a certain range, the water turbine may malfunction, causing serious accidents resulting in casualties and property damage. Therefore, real-time monitoring of the vibration displacement of water turbines is of great importance to ensure the safe operation of water turbines. Existing vibration displacement calculation methods do not consider the influence of vibration frequency, resulting in significant errors when measuring complex vibration displacements. This paper proposes a grating wavelength conversion method based on Fourier series. First, the wavelength variation of the fiber-optic grating sensor feedback is decomposed into multiple wavelength components containing only a single frequency according to different frequencies. Calculate the vibration acceleration generated by each wavelength component corresponding to the vibration component based on the sensitivity of the sensor. Calculate the vibration displacement generated by each vibration component by quadratic integration of the vibration acceleration. Sum these vibration displacements to obtain the total vibration displacement. Perform displacement measurement for complex vibrations. In order to improve the accuracy of vibration displacement measurement, we conducted calibration experiments on the sensitivity outside the stable operating frequency band of fiber-optic grating sensors, because only a part of the corresponding relationship between vibration frequency and sensitivity is included in the stable operating frequency band. And the corresponding relationship between vibration frequency and wavelength change in this part of the frequency was determined by segmented fitting. Two experiments were designed to compare the ability of this method to calculate vibration displacement with traditional methods. They are the simple harmonic vibration experiment and the complex vibration experiment. The harmonic vibration experiment provides vibration excitation of only a single frequency by a vibration table. In the complex vibration experiment, the vibration excitation generated by the vibration table contains two different vibration frequencies, and there is a certain phase difference between these two different vibration frequencies. The wavelength change of the fiber-optic grating sensor is recorded, and the vibration acceleration and displacement are calculated using the above two methods. In the harmonic vibration experiment, the maximum vibration acceleration error of the traditional method is 4.6%, and the maximum vibration displacement error is 4.6%. The maximum vibration acceleration error of the method in this article is 2.1%, and the maximum vibration displacement error is 3.74%. In complex vibration experiments, the traditional method cannot accurately measure the vibration displacement, and the maximum error of this method is 8.45%. The experimental results of harmonic vibration show that both traditional methods and grating wavelength conversion methods based on Fourier series can accurately measure vibration displacement when there is only a single frequency vibration in the environment. The results of complex vibration experiments show that when there are multiple vibrations of different frequencies in the environment, the grating wavelength conversion method based on Fourier series can accurately decompose and reconstruct the grating wavelength signal, and has higher accuracy and stronger stability in measuring multi-frequency vibration displacement.In addition, we also used these two methods to measure the vibration displacement peak-to-peak values of the stator end and upper frame data of the water turbine, and compared them with the results of the electrical sensors. The experimental results show that the vibration component at the stator end of the water turbine is single, and the vibration displacement peak-to-peak measurement results are similar. The vibration components at the upper frame of the water turbine are complex, and the vibration displacement peak-to-peak measurement results of the traditional method are less than 5 μm. The vibration displacement peak-to-peak measurement results of the electric sensors and the grating wavelength conversion methods based on Fourier series are both around 45 μm. This indicates that the grating wavelength conversion method based on Fourier series has certain practical application value. During operation of a water turbine, the vibration of its mechanical structure may reflect its operating condition. If the peak value of the vibration displacement exceeds a certain range, the water turbine may malfunction, causing serious accidents resulting in casualties and property damage. Therefore, real-time monitoring of the vibration displacement of water turbines is of great importance to ensure the safe operation of water turbines. Existing vibration displacement calculation methods do not consider the influence of vibration frequency, resulting in significant errors when measuring complex vibration displacements. This paper proposes a grating wavelength conversion method based on Fourier series. First, the wavelength variation of the fiber-optic grating sensor feedback is decomposed into multiple wavelength components containing only a single frequency according to different frequencies. Calculate the vibration acceleration generated by each wavelength component corresponding to the vibration component based on the sensitivity of the sensor. Calculate the vibration displacement generated by each vibration component by quadratic integration of the vibration acceleration. Sum these vibration displacements to obtain the total vibration displacement. Perform displacement measurement for complex vibrations. In order to improve the accuracy of vibration displacement measurement, we conducted calibration experiments on the sensitivity outside the stable operating frequency band of fiber-optic grating sensors, because only a part of the corresponding relationship between vibration frequency and sensitivity is included in the stable operating frequency band. And the corresponding relationship between vibration frequency and wavelength change in this part of the frequency was determined by segmented fitting. Two experiments were designed to compare the ability of this method to calculate vibration displacement with traditional methods. They are the simple harmonic vibration experiment and the complex vibration experiment. The harmonic vibration experiment provides vibration excitation of only a single frequency by a vibration table. In the complex vibration experiment, the vibration excitation generated by the vibration table contains two different vibration frequencies, and there is a certain phase difference between these two different vibration frequencies. The wavelength change of the fiber-optic grating sensor is recorded, and the vibration acceleration and displacement are calculated using the above two methods. In the harmonic vibration experiment, the maximum vibration acceleration error of the traditional method is 4.6%, and the maximum vibration displacement error is 4.6%. The maximum vibration acceleration error of the method in this article is 2.1%, and the maximum vibration displacement error is 3.74%. In complex vibration experiments, the traditional method cannot accurately measure the vibration displacement, and the maximum error of this method is 8.45%. The experimental results of harmonic vibration show that both traditional methods and grating wavelength conversion methods based on Fourier series can accurately measure vibration displacement when there is only a single frequency vibration in the environment. The results of complex vibration experiments show that when there are multiple vibrations of different frequencies in the environment, the grating wavelength conversion method based on Fourier series can accurately decompose and reconstruct the grating wavelength signal, and has higher accuracy and stronger stability in measuring multi-frequency vibration displacement.In addition, we also used these two methods to measure the vibration displacement peak-to-peak values of the stator end and upper frame data of the water turbine, and compared them with the results of the electrical sensors. The experimental results show that the vibration component at the stator end of the water turbine is single, and the vibration displacement peak-to-peak measurement results are similar. The vibration components at the upper frame of the water turbine are complex, and the vibration displacement peak-to-peak measurement results of the traditional method are less than 5 μm. The vibration displacement peak-to-peak measurement results of the electric sensors and the grating wavelength conversion methods based on Fourier series are both around 45 μm. This indicates that the grating wavelength conversion method based on Fourier series has certain practical application value.
Acta Photonica Sinica
- Publication Date: Aug. 25, 2024
- Vol. 53, Issue 8, 0806002 (2024)
High Coupling Efficiency Mode Field Adapter with Low NA LMA Fiber
Feng XIONG, Wei MU, Yang WANG, Yunliang MA... and Xiaobei ZHANG|Show fewer author(s)
Transverse Mode Instability (TMI) and Nonlinear Optical Effects (NLE) prevent high-power all-fiber lasers from further power scaling. Low Numerical Aperture (NA) Large Mode Area (LMA) active fibers can maintain large effective areas while suppressing the Higher-Order Modes (HOMs) and increasing the thresholds of NLE and TMI. Additionally, it is easier to match the passive component for low NA LMA fiber because its structure is simpler and consistent with the step-index fiber structure. Mode Field Adapters (MFAs) match the mode field between LMA fibers and Single-Mode Fibers (SMFs) and are crucial passive components in fiber laser systems. The insertion loss and beam quality of MFAs significantly affect the power scaling and beam quality of laser systems. This paper made MFA based on tapered low NA LMA fiber with NA=0.05 and Thermally Expanded Core (TEC) fibers, maintaining high coupling efficiency and improving the output beam quality. The impact of mode field mismatch, core offset, and angular misalignment between LMA fibers with different NAs and SMF on coupling efficiency and beam quality of MFA was studied theoretically and experimentally. First, theoretical models for TEC and tapered LMA fibers were built based on diffusion and adiabatic criterion equations. The mode field distribution and propagation characteristics of TEC and tapered LMA fibers were simulated based on the beam propagation method to optimize the device structure and preparation parameters. Second, a simulation model was created to analyze the insertion loss and beam quality degradation caused by mode field mismatch, core offset, and angular misalignment between LMA fibers with different NAs and SMFs. The simulation results show that reducing the NA of LMA fibers helps suppress HOMs and reduces the insertion loss and beam quality degradation of MFAs caused by core offset and angular misalignment. During the experimental process, the SMF was heated with an H2-O2 flame to expand the Mode Field Diameter (MFD) of SMF without causing transmission loss. The MFD of the TEC SMF under different heating times was measured using the far-field method. Two MFAs were prepared by LMA fibers (25/400 μm) with NAs of 0.06 and 0.05, respectively, to TEC SMF (5.3/125 μm, NA=0.14). The insertion loss and beam quality factor (M2) of the devices were measured. The forward insertion loss decreased from 4.50 dB to 0.29 dB, and the difference in bi-directional insertion loss decreased from 2.50 dB to 0.19 dB when the LMA fiber (NA=0.06) for MFD matched with the SMF. The experimental results show that matching the MFD of SMF and LMA fibers effectively reduces the insertion loss and the difference in bi-directional insertion loss. The taper ratios of the LMA fibers are both 2, and the heating times for the SMF are 25 min and 20 min, respectively, when the MFD is matched between the LMA fibers with NAs of 0.05 and 0.06 and SMF. Due to the MFD matching between the LMA fibers and SMFs, only the unavoidable core offset and angular misalignment during the fusion process, which affect the coupling efficiency and beam quality, are considered, and these misalignments are random variables. The impact of misalignment on beam quality and coupling efficiency in LMA fibers with different NAs was indirectly reflected by performing multiple measurements and calculating the mean and standard deviation. The forward insertion loss decreases to 0.29 dB with a standard deviation of 0.085, and the bi-directional insertion loss difference is 0.19 dB with a standard deviation of 0.077 when the MFD is matched between the LMA (NA=0.06) fiber and the SMF. The forward insertion loss decreases to 0.23 dB with a standard deviation of 0.024, and the bi-directional insertion loss difference is 0.06 dB with a standard deviation of 0.011 when the MFD is matched between the LMA (NA=0.05) fiber and SMF. Cladding modes caused the difference in bi-directional insertion loss, and HOMs were not stripped by the cladding light strippers as they do not propagate in SMF. Therefore, this difference is positively correlated with the beam quality of the MFA. The difference in bi-directional insertion loss for the MFAs based on LMA fibers with NAs of 0.05 and 0.06 are 1.76 dB and 2.50 dB, and the M2 are 1.88 and 2.15, respectively, when the MFD of the SMF and the LMA fiber are not matched. This difference for the MFAs based on LMA fibers with NAs of 0.05 and 0.06 is 0.06 dB and 0.19 dB, and the M2 value is 1.15 with a standard deviation of 0.017 and 1.26 with a standard deviation of 0.092, respectively, and when the MFD of the SMF and the LMA fiber are matched. The experimental results show that the LMA fiber with NA=0.05 contains fewer HOMs and reduces beam quality degradation and insertion loss caused by core offset and angular misalignment during the splicing process, resulting in higher beam quality and lower insertion loss of the MFA. These conclusions are consistent with the theoretical analysis and simulation results. The MFA based on LMA fiber with NA=0.05 has promising application prospects in single-mode output high-power fiber lasers due to its low insertion loss and high beam quality advantages. Transverse Mode Instability (TMI) and Nonlinear Optical Effects (NLE) prevent high-power all-fiber lasers from further power scaling. Low Numerical Aperture (NA) Large Mode Area (LMA) active fibers can maintain large effective areas while suppressing the Higher-Order Modes (HOMs) and increasing the thresholds of NLE and TMI. Additionally, it is easier to match the passive component for low NA LMA fiber because its structure is simpler and consistent with the step-index fiber structure. Mode Field Adapters (MFAs) match the mode field between LMA fibers and Single-Mode Fibers (SMFs) and are crucial passive components in fiber laser systems. The insertion loss and beam quality of MFAs significantly affect the power scaling and beam quality of laser systems. This paper made MFA based on tapered low NA LMA fiber with NA=0.05 and Thermally Expanded Core (TEC) fibers, maintaining high coupling efficiency and improving the output beam quality. The impact of mode field mismatch, core offset, and angular misalignment between LMA fibers with different NAs and SMF on coupling efficiency and beam quality of MFA was studied theoretically and experimentally. First, theoretical models for TEC and tapered LMA fibers were built based on diffusion and adiabatic criterion equations. The mode field distribution and propagation characteristics of TEC and tapered LMA fibers were simulated based on the beam propagation method to optimize the device structure and preparation parameters. Second, a simulation model was created to analyze the insertion loss and beam quality degradation caused by mode field mismatch, core offset, and angular misalignment between LMA fibers with different NAs and SMFs. The simulation results show that reducing the NA of LMA fibers helps suppress HOMs and reduces the insertion loss and beam quality degradation of MFAs caused by core offset and angular misalignment. During the experimental process, the SMF was heated with an H2-O2 flame to expand the Mode Field Diameter (MFD) of SMF without causing transmission loss. The MFD of the TEC SMF under different heating times was measured using the far-field method. Two MFAs were prepared by LMA fibers (25/400 μm) with NAs of 0.06 and 0.05, respectively, to TEC SMF (5.3/125 μm, NA=0.14). The insertion loss and beam quality factor (M2) of the devices were measured. The forward insertion loss decreased from 4.50 dB to 0.29 dB, and the difference in bi-directional insertion loss decreased from 2.50 dB to 0.19 dB when the LMA fiber (NA=0.06) for MFD matched with the SMF. The experimental results show that matching the MFD of SMF and LMA fibers effectively reduces the insertion loss and the difference in bi-directional insertion loss. The taper ratios of the LMA fibers are both 2, and the heating times for the SMF are 25 min and 20 min, respectively, when the MFD is matched between the LMA fibers with NAs of 0.05 and 0.06 and SMF. Due to the MFD matching between the LMA fibers and SMFs, only the unavoidable core offset and angular misalignment during the fusion process, which affect the coupling efficiency and beam quality, are considered, and these misalignments are random variables. The impact of misalignment on beam quality and coupling efficiency in LMA fibers with different NAs was indirectly reflected by performing multiple measurements and calculating the mean and standard deviation. The forward insertion loss decreases to 0.29 dB with a standard deviation of 0.085, and the bi-directional insertion loss difference is 0.19 dB with a standard deviation of 0.077 when the MFD is matched between the LMA (NA=0.06) fiber and the SMF. The forward insertion loss decreases to 0.23 dB with a standard deviation of 0.024, and the bi-directional insertion loss difference is 0.06 dB with a standard deviation of 0.011 when the MFD is matched between the LMA (NA=0.05) fiber and SMF. Cladding modes caused the difference in bi-directional insertion loss, and HOMs were not stripped by the cladding light strippers as they do not propagate in SMF. Therefore, this difference is positively correlated with the beam quality of the MFA. The difference in bi-directional insertion loss for the MFAs based on LMA fibers with NAs of 0.05 and 0.06 are 1.76 dB and 2.50 dB, and the M2 are 1.88 and 2.15, respectively, when the MFD of the SMF and the LMA fiber are not matched. This difference for the MFAs based on LMA fibers with NAs of 0.05 and 0.06 is 0.06 dB and 0.19 dB, and the M2 value is 1.15 with a standard deviation of 0.017 and 1.26 with a standard deviation of 0.092, respectively, and when the MFD of the SMF and the LMA fiber are matched. The experimental results show that the LMA fiber with NA=0.05 contains fewer HOMs and reduces beam quality degradation and insertion loss caused by core offset and angular misalignment during the splicing process, resulting in higher beam quality and lower insertion loss of the MFA. These conclusions are consistent with the theoretical analysis and simulation results. The MFA based on LMA fiber with NA=0.05 has promising application prospects in single-mode output high-power fiber lasers due to its low insertion loss and high beam quality advantages.
Acta Photonica Sinica
- Publication Date: Aug. 25, 2024
- Vol. 53, Issue 8, 0806001 (2024)
Deep Learning Aided Signal Detection Algorithm Experimental Research for Underwater Optical Communication
Pengfei YE, Peng ZHANG, Hao YU, Shuang HE... and Shoufeng TONG|Show fewer author(s)
Underwater wireless optical communication has garnered significant attention in the wireless communication field due to its high data rate, enhanced security, and lightweight nature. However, seawater can induce absorption and scattering of light. Absorption results in a reduction of the received optical power at the receiver, which is an irreversible process, while scattering causes alterations in the received photons at the receiver. Moreover, the ocean typically contains turbulence, a phenomenon caused by temperature variations and irregular movements, leading to random fluctuations in the optical signal. Consequently, the underwater channel is intricate and challenging to predict. To achieve reliable communication performance, a more dependable signal detection method is required at the receiver. In this study, a deep learning-assisted signal detection method is proposed for underwater optical communication. A convolutional neural network (a specialized form of deep neural network) is developed to directly detect the Original On-off Keying (OOK) signal, and two distinct training methods for the Deep Neural Network (DNN) are proposed during the training phase. Initially, an indoor underwater optical communication experimental platform is designed and constructed, incorporating three types of water tank channels (flowing water, turbid flow 1, turbid flow 2). The attenuation coefficients and probability density functions of the channels are measured. Subsequently, a simulated underwater optical channel is derived based on the measured channel mathematical models, and a simulated dataset of OOK signals for the neural network is obtained. The proposed methods are tested using the dataset, and the performance of the two different DNN training methods and the adaptive threshold method is simulated under different simulated channels. The proposed methods exhibit an improvement in Bit Error Rate (BER) compared to the adaptive threshold method at any signal-to-noise ratio in the three channels. The improvement is most notable in the simplest flow channel, with up to a two-order-of-magnitude enhancement, and it increases with higher signal-to-noise ratios in the relatively complex turbid flow channel 2. Additionally, due to DNN training method 1 learning multiple datasets from different channels, it exhibits worse BER performance compared to training method 2, which only learns one channel dataset. However, thanks to the powerful fitting capability of DNN, the BER is still superior to the adaptive threshold method. To validate the simulation results, experimental datasets of OOK signals are obtained based on the experimental platform. The DNN is retrained and tested using the experimental datasets, and the BER performance of the two different DNN training methods and the adaptive threshold method is experimentally studied. For 5 Mbps communication transmission in the three water tank channels, the DNN method achieves a reduction in BER of two orders of magnitude, one order of magnitude, and one order of magnitude, respectively, compared to the adaptive threshold method. The trend of the experimental results is consistent with the simulation. For turbid flow 1 and different communication rates (5 Mbps, 10 Mbps, 25 Mbps), the DNN method achieves a reduction in BER of one order of magnitude at all three rates, and the proposed method requires lower received optical power compared to the adaptive threshold method when the BER is the same. The simulation and experimental results demonstrate that the proposed method enhances the performance of underwater wireless optical communication in complex channels compared to the adaptive threshold method, validating the reliability of the method. Therefore, the method can offer valuable insights for the design of high-speed and reliable underwater wireless optical communication systems. Underwater wireless optical communication has garnered significant attention in the wireless communication field due to its high data rate, enhanced security, and lightweight nature. However, seawater can induce absorption and scattering of light. Absorption results in a reduction of the received optical power at the receiver, which is an irreversible process, while scattering causes alterations in the received photons at the receiver. Moreover, the ocean typically contains turbulence, a phenomenon caused by temperature variations and irregular movements, leading to random fluctuations in the optical signal. Consequently, the underwater channel is intricate and challenging to predict. To achieve reliable communication performance, a more dependable signal detection method is required at the receiver. In this study, a deep learning-assisted signal detection method is proposed for underwater optical communication. A convolutional neural network (a specialized form of deep neural network) is developed to directly detect the Original On-off Keying (OOK) signal, and two distinct training methods for the Deep Neural Network (DNN) are proposed during the training phase. Initially, an indoor underwater optical communication experimental platform is designed and constructed, incorporating three types of water tank channels (flowing water, turbid flow 1, turbid flow 2). The attenuation coefficients and probability density functions of the channels are measured. Subsequently, a simulated underwater optical channel is derived based on the measured channel mathematical models, and a simulated dataset of OOK signals for the neural network is obtained. The proposed methods are tested using the dataset, and the performance of the two different DNN training methods and the adaptive threshold method is simulated under different simulated channels. The proposed methods exhibit an improvement in Bit Error Rate (BER) compared to the adaptive threshold method at any signal-to-noise ratio in the three channels. The improvement is most notable in the simplest flow channel, with up to a two-order-of-magnitude enhancement, and it increases with higher signal-to-noise ratios in the relatively complex turbid flow channel 2. Additionally, due to DNN training method 1 learning multiple datasets from different channels, it exhibits worse BER performance compared to training method 2, which only learns one channel dataset. However, thanks to the powerful fitting capability of DNN, the BER is still superior to the adaptive threshold method. To validate the simulation results, experimental datasets of OOK signals are obtained based on the experimental platform. The DNN is retrained and tested using the experimental datasets, and the BER performance of the two different DNN training methods and the adaptive threshold method is experimentally studied. For 5 Mbps communication transmission in the three water tank channels, the DNN method achieves a reduction in BER of two orders of magnitude, one order of magnitude, and one order of magnitude, respectively, compared to the adaptive threshold method. The trend of the experimental results is consistent with the simulation. For turbid flow 1 and different communication rates (5 Mbps, 10 Mbps, 25 Mbps), the DNN method achieves a reduction in BER of one order of magnitude at all three rates, and the proposed method requires lower received optical power compared to the adaptive threshold method when the BER is the same. The simulation and experimental results demonstrate that the proposed method enhances the performance of underwater wireless optical communication in complex channels compared to the adaptive threshold method, validating the reliability of the method. Therefore, the method can offer valuable insights for the design of high-speed and reliable underwater wireless optical communication systems.
Acta Photonica Sinica
- Publication Date: Jul. 25, 2024
- Vol. 53, Issue 7, 0706001 (2024)
High Anti-noise Displacement Sensor Base on Fiber Bragg Grating Peak Counting
Haohao FAN, Qiang ZHAO, Dawei DU, Peng SUN... and Mingtao CHEN|Show fewer author(s)
The lateral creep of hydrate-bearing layers associated with natural gas hydrate decomposition can easily cause geo-engineering security risks. The triaxial shear instrument is an experimental apparatus for simulating lateral creep of the lateral creep of hydrate-bearing layers. To obtain the displacement of this creep accurately and in real time, a kind of displacement sensing scheme based on fiber Brag grating peak counting of strain shift curve with high noise resistance is proposed. The sensor is mainly composed of fiber Bragg grating, cantilever, transmission system, gear, rack, base and metal-tube. Inside of the transmission system is composed of gears with different teeth and installed on the base with bolts. Using the rack as the probe to engage with the input gear of the transmission system, another side of output shaft is fixed with the gear securely. One end of the cantilever beam press is fixed to the side plate of the base, while the other end presses against the gear. One end of the fiber Bragg grating is encapsulated in a metal tube using epoxy resin, and the tube is welded vertically to the free end of the cantilever beam press plate using a laser, while the other end is fixed in a similar manner on the side plate of the base after a pre-tension is applied. The interval between two metal tubes is 41 mm. The rack and transmission system are used to convert the lateral displacement of the hydrate-bearing layers into the rotation of the output gear. During the rotation of gear, the tooth of gear pulls on the free end of the cantilever to produce periodic stretching and resetting of the fiber Bragg grating. The average and minimum wavelength shift of the reflection center is 1405 pm and 1263 pm, respectively. By calculating the product of the number of peak jumps in the wavelength drift curve of the reflection center of the fiber Bragg grating and the standard step length of the rack passing between adjacent peak jump, we can determine the lateral displacement of the geological layer. Furthermore, we can build a linear relationship between the frequency of peak jumps and speed. The coefficient of sensor's transmission system is 2 mm/r and tooth number of gear is 20, respectively. In the measuring range of 40 mm, displacement sensor has a resolution up to 0.1 mm, maximum error of only 16 μm, and the range is adjustable through the rack length. The sensor displays excellent repeatability and multi-parameter detection capabilities, greatly enhancing the sensor's noise immunity. When the rack is pushed at three different speeds for 6 mm, it is found that the wavelength drift for the same number of gear teeth is only 28 pm, which is significantly smaller than the minimum wavelength drift of the reflection center 1 263 pm. In situations where the displacement change speed far exceeds temperature variations, temperature only offsets the entire wavelength drift curve of the reflection center. This offset is directly related to the temperature sensitivity of the fiber Bragg grating and range, temperature does not impact the wavelength drift of the reflection center resulting from displacement or the displacement of the rack between adjacent peaks. In this case, the fiber Bragg grating is only affected by noise sources like vibration and demodulation instrument error. The minimum optical signal-to-noise ratio achieved is 43.966 dB, meeting the requirements for hydrate-bearing layer creep monitoring and enabling simultaneous measurement of displacement and velocity. It's worth noting that when the displacement change speed matches the temperature change specifically, when the temperature changes rapidly form 0 ℃ to 20 ℃, the optical signal-to-noise ratio decreases from 43.966 dB to 14.497 dB, greatly impacting displacement measurement accuracy. Therefore, it's essential to incorporate a free-state fiber Bragg grating for temperature compensation, ensuring the sensor maintains a high noise resistance. At this point, the sensor utilizes two fiber Bragg gratings to achieve three-parameter measurement of displacement, speed, and temperature, while maintaining a high cost-efficiency. Finally, four kinds of displacement sensors' performance are compared, with displacement sensor base on fiber Brag grating peak counting of strain shift curve standing out for its superior anti-noise and multi-parameter detection capabilities. The lateral creep of hydrate-bearing layers associated with natural gas hydrate decomposition can easily cause geo-engineering security risks. The triaxial shear instrument is an experimental apparatus for simulating lateral creep of the lateral creep of hydrate-bearing layers. To obtain the displacement of this creep accurately and in real time, a kind of displacement sensing scheme based on fiber Brag grating peak counting of strain shift curve with high noise resistance is proposed. The sensor is mainly composed of fiber Bragg grating, cantilever, transmission system, gear, rack, base and metal-tube. Inside of the transmission system is composed of gears with different teeth and installed on the base with bolts. Using the rack as the probe to engage with the input gear of the transmission system, another side of output shaft is fixed with the gear securely. One end of the cantilever beam press is fixed to the side plate of the base, while the other end presses against the gear. One end of the fiber Bragg grating is encapsulated in a metal tube using epoxy resin, and the tube is welded vertically to the free end of the cantilever beam press plate using a laser, while the other end is fixed in a similar manner on the side plate of the base after a pre-tension is applied. The interval between two metal tubes is 41 mm. The rack and transmission system are used to convert the lateral displacement of the hydrate-bearing layers into the rotation of the output gear. During the rotation of gear, the tooth of gear pulls on the free end of the cantilever to produce periodic stretching and resetting of the fiber Bragg grating. The average and minimum wavelength shift of the reflection center is 1405 pm and 1263 pm, respectively. By calculating the product of the number of peak jumps in the wavelength drift curve of the reflection center of the fiber Bragg grating and the standard step length of the rack passing between adjacent peak jump, we can determine the lateral displacement of the geological layer. Furthermore, we can build a linear relationship between the frequency of peak jumps and speed. The coefficient of sensor's transmission system is 2 mm/r and tooth number of gear is 20, respectively. In the measuring range of 40 mm, displacement sensor has a resolution up to 0.1 mm, maximum error of only 16 μm, and the range is adjustable through the rack length. The sensor displays excellent repeatability and multi-parameter detection capabilities, greatly enhancing the sensor's noise immunity. When the rack is pushed at three different speeds for 6 mm, it is found that the wavelength drift for the same number of gear teeth is only 28 pm, which is significantly smaller than the minimum wavelength drift of the reflection center 1 263 pm. In situations where the displacement change speed far exceeds temperature variations, temperature only offsets the entire wavelength drift curve of the reflection center. This offset is directly related to the temperature sensitivity of the fiber Bragg grating and range, temperature does not impact the wavelength drift of the reflection center resulting from displacement or the displacement of the rack between adjacent peaks. In this case, the fiber Bragg grating is only affected by noise sources like vibration and demodulation instrument error. The minimum optical signal-to-noise ratio achieved is 43.966 dB, meeting the requirements for hydrate-bearing layer creep monitoring and enabling simultaneous measurement of displacement and velocity. It's worth noting that when the displacement change speed matches the temperature change specifically, when the temperature changes rapidly form 0 ℃ to 20 ℃, the optical signal-to-noise ratio decreases from 43.966 dB to 14.497 dB, greatly impacting displacement measurement accuracy. Therefore, it's essential to incorporate a free-state fiber Bragg grating for temperature compensation, ensuring the sensor maintains a high noise resistance. At this point, the sensor utilizes two fiber Bragg gratings to achieve three-parameter measurement of displacement, speed, and temperature, while maintaining a high cost-efficiency. Finally, four kinds of displacement sensors' performance are compared, with displacement sensor base on fiber Brag grating peak counting of strain shift curve standing out for its superior anti-noise and multi-parameter detection capabilities.
Acta Photonica Sinica
- Publication Date: Jun. 25, 2024
- Vol. 53, Issue 6, 0606003 (2024)
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