SNR Enhancement for Comparator-Based Ultra-Low-Sampling Φ-OTDR System Using Compressed Sensing

被引:1
|
作者
Xiao, Zhenyu [1 ,2 ]
Li, Xiaoming [3 ]
Zhang, Haofei [3 ]
Yuan, Xueguang [1 ,2 ]
Zhang, Yang-An [1 ,2 ]
Zhang, Yuan [1 ,2 ]
Li, Zhengyang [1 ,2 ]
Wang, Qi [1 ,2 ]
Huang, Yongqing [1 ,2 ]
机构
[1] Beijing Univ Posts & Telecommun, State Key Lab Informat Photon & Opt Commun, Beijing 100876, Peoples R China
[2] Beijing Univ Posts & Telecommun, Sch Elect Engn, Beijing 100876, Peoples R China
[3] 208 Res Inst China Ordnance Ind, Beijing 102202, Peoples R China
关键词
compressed sensing; phase-sensitive optical time-domain reflectometry; signal-to-noise ratio; ultra-low sampling; data volume; FIBEROPTIC ACOUSTIC SENSOR; VIBRATION SENSOR; RECOGNITION; RESOLUTION;
D O I
10.3390/s24113279
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
The large amount of sampled data in coherent phase-sensitive optical time-domain reflectometry (Phi-OTDR) brings heavy data transmission, processing, and storage burdens. By using the comparator combined with undersampling, we achieve simultaneous reduction of sampling rate and sampling resolution in hardware, thus greatly decreasing the sampled data volume. But this way will inevitably cause the deterioration of detection signal-to-noise ratio (SNR) due to the quantization noise's dramatic increase. To address this problem, denoising the demodulated phase signals using compressed sensing, which exploits the sparsity of spectrally sparse vibration, is proposed, thereby effectively enhancing the detection SNR. In experiments, the comparator with a sampling parameter of 62.5 MS/s and 1 bit successfully captures the 80 MHz beat signal, where the sampled data volume per second is only 7.45 MB. Then, when the piezoelectric transducer's driving voltage is 1 Vpp, 300 mVpp, and 100 mVpp respectively, the SNRs of the reconstructed 200 Hz sinusoidal signals are respectively enhanced by 23.7 dB, 26.1 dB, and 28.7 dB by using compressed sensing. Moreover, multi-frequency vibrations can also be accurately reconstructed with a high SNR. Therefore, the proposed technique can effectively enhance the system's performance while greatly reducing its hardware burden.
引用
收藏
页数:12
相关论文
共 29 条
  • [21] Cardiac diffusion tensor imaging based on compressed sensing using joint sparsity and low-rank approximation
    Huang, Jianping
    Wang, Lihui
    Chu, Chunyu
    Zhang, Yanli
    Liu, Wanyu
    Zhu, Yuemin
    TECHNOLOGY AND HEALTH CARE, 2016, 24 : S593 - S599
  • [22] A Low-Complexity Hardware Implementation of Compressed Sensing-Based Channel Estimation for ISDB-T System
    Ferdian, Rian
    Hou, Yafei
    Okada, Minoru
    IEEE TRANSACTIONS ON BROADCASTING, 2017, 63 (01) : 92 - 102
  • [23] Performance of IRS-Assisted MIMO THz System Using Compressed Sensing-Based Measurement Matrix
    Sharma, Vaishali
    Kumar, Deepak
    Sharma, Sanjeev
    Bhatia, Vimal
    Krejcar, Ondrej
    Brida, Peter
    IEEE ACCESS, 2024, 12 : 144950 - 144964
  • [24] Reducing footprint of unit selection based text-to-speech system using compressed sensing and sparse representation
    Sharma, Pulkit
    Abrol, Vinayak
    Nivedita
    Sao, Anil Kumar
    COMPUTER SPEECH AND LANGUAGE, 2018, 52 : 191 - 208
  • [25] A General Framework for Compressed Sensing and Parallel MRI Using Annihilating Filter Based Low-Rank Hankel Matrix
    Jin, Kyong Hwan
    Lee, Dongwook
    Ye, Jong Chul
    IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING, 2016, 2 (04): : 480 - 495
  • [26] Compressed Sensing based object detection and tracking system using Measurement Selection Process for Wireless Visual Sensor Networks
    Nandhini, S. Aasha
    Radha, S.
    PROCEEDINGS OF THE 2016 IEEE INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, SIGNAL PROCESSING AND NETWORKING (WISPNET), 2016, : 1117 - 1122
  • [27] Industrial x-ray inspection system with improved image characterization using blind deblurring based on compressed-sensing scheme
    Kim, Kyuseok
    Park, Soyoung
    Kim, Guna
    Cho, Hyosung
    Je, Uikyu
    Park, Chulkyu
    Lim, Hyunwoo
    Lee, Hunwoo
    Lee, Dongyeon
    Park, Yeonok
    Woo, Taeho
    INSTRUMENTATION SCIENCE & TECHNOLOGY, 2017, 45 (03) : 248 - 258
  • [28] A Context-Aware Readout System for Sparse Touch Sensing Array Using Ultra-Low-Power Always-On Event Detection
    Roh H.
    Choi W.-S.
    IEEE Transactions on Circuits and Systems II: Express Briefs, 2023, 70 (09) : 3719 - 3723
  • [29] Improved Automatic Speech Recognition System by using Compressed Sensing Signal Reconstruction based on L0 and L1 estimation algorithms
    Gavrilescu, Mihai
    PROCEEDINGS OF THE 2015 7TH INTERNATIONAL CONFERENCE ON ELECTRONICS, COMPUTERS AND ARTIFICIAL INTELLIGENCE (ECAI), 2015, : S23 - S27