基于fpga的PGC解调中载波相位延迟和调制深度的快速补偿

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Bingtao Cai;Wenzhe Xiao;Xiaobao Chen;Caoyuan Wang;Limin Xiao
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引用次数: 0

摘要

相位产生载波(PGC)解调已成为光纤干涉传感器(FOISs)的主要解调方法。实际上,动态光程延迟和环境光程差(OPD)变化会导致PGC解调的关键参数:载波相位延迟($\theta $)和调制深度(C)发生漂移,从而降低精度。本文提出了一种新的基于fpga的PGC算法,该算法结合了正交多频混合(MFM)技术,通过两个关键创新解决了这些限制:相位延迟消除(EPD)模块实现了一种新颖的符号位生成机制,实现了完整的0°-360°相位补偿,同时消除了$2\pi $ -幅度的相位跳变和调制深度补偿(CMD)模块,该模块动态计算${J}_{{2}}\text {(}{C}\text {)}/{J}_{{1}}\text {(}{C}\text {)}$贝塞尔比,主动抑制谐波失真。数值仿真验证了该算法在不同$\theta $ (${0}\pi $ - $2\pi $)和C (1.0-3.5 rad)条件下对畸变的高速抑制。实验结果显示了显著的性能改进,包括32.9 db的信噪比(SINAD),比传统方法提高21.5 db,总谐波失真(THD)为1.81% (17.6-dB enhancement). The design achieves 98.6% frequency-domain linearity. Leveraging the parallel processing capability of the field-programmable gate array (FPGA), this solution achieves real-time compensation at a throughput of one sample point per clock cycle, making it particularly suitable for large-scale FOIS deployments, including underwater acoustic monitoring and other precision sensing applications.
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FPGA-Based Rapid Compensation of Carrier Phase Delay and Modulation Depth in PGC Demodulation
The phase-generated carrier (PGC) demodulation has emerged as a predominant demodulation method for fiber-optic interferometric sensors (FOISs). In practice, dynamic optical path delays and environmental optical path difference (OPD) variations induce drift in PGC demodulation’s key parameters: carrier phase delay ( $\theta $ ) and modulation depth (C), degrading accuracy. This article presents a novel FPGA-based PGC algorithm incorporating quadrature multifrequency mixing (MFM) technology, which addresses these limitations through two key innovations: an elimination of phase delay (EPD) module implementing a novel sign-bit generation mechanism that enables complete 0°–360° phase compensation while eliminating $2\pi $ -magnitude phase jumps and compensation of modulation depth (CMD) module that dynamically computes the ${J}_{{2}}\text {(}{C}\text {)}/{J}_{{1}}\text {(}{C}\text {)}$ Bessel ratio to actively suppress harmonic distortions. Numerical simulations verify the algorithm’s high-speed suppression of distortions under varying $\theta $ ( ${0}\pi $ $2\pi $ ) and C (1.0–3.5 rad) conditions. Experimental results demonstrate significant performance enhancements, including a 32.9-dB signal-to-noise-and-distortion (SINAD) ratio, a 21.5-dB improvement over conventional methods, and total harmonic distortion (THD) of 1.81% (17.6-dB enhancement). The design achieves 98.6% frequency-domain linearity. Leveraging the parallel processing capability of the field-programmable gate array (FPGA), this solution achieves real-time compensation at a throughput of one sample point per clock cycle, making it particularly suitable for large-scale FOIS deployments, including underwater acoustic monitoring and other precision sensing applications.
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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