低相干干涉测量数据的新型数字信号处理方法

IF 4.3 2区综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Sensors Journal Pub Date : 2024-10-28 DOI:10.1109/JSEN.2024.3483433

Paulo Robalinho;António Vaz Rodrigues;Susana Novais;António Lobo Ribeiro;Susana Silva;Orlando Frazão

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引用次数: 0

摘要

这项工作的目的是引入一种新的数字信号处理方法，用于使用低相干干涉法（LCI）以1 khz执行器振荡频率获得的数据。采用卷积和相关运算作为有效滤波，降低了多层滤波的计算复杂度。提出了一种包络滤波技术，以解决由非线性执行机构运动引起的峰值信号测定误差。此外，还提出了一种相位线性化方法来对峰值相对于执行器信号的位置进行归一化。实验结果表明，信噪比（SNR）提高了50 dB。长期测量表明，频率低于1mhz时噪声降低11db。这项研究使LCI在至少1 kHz的采样率下实现，并扩展了其在极端测量条件下的适用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Novel Digital Signal Processing Method for Data Acquired From Low Coherence Interferometry

The aim of this work is to introduce a novel digital signal processing method for data acquired using low coherence interferometry (LCI) with a 1-kHz actuator oscillation frequency. Convolution and correlation operations are employed as efficient filters, reducing computational complexity for multilayer filtering. An envelope filtering technique is developed to address discrepancies in peak signal determination caused by nonlinear actuator motion. Additionally, a phase linearization method is presented to normalize the peak position relative to the actuator signal. Experimental results demonstrate a significant signal-to-noise ratio (SNR) improvement of 50 dB. Long-term measurements reveal an 11-dB noise reduction for frequencies below 1 mHz. This research enables LCI implementation at sampling rates of at least 1 kHz and expands its applicability to extreme measurement conditions.

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来源期刊

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