Uncertainty of signal reconstruction in the case of jittery and noisy measurements

被引:22
|
作者
Dabóczi, T [1 ]
机构
[1] Tech Univ Budapest, Dept Measurement & Informat Syst, H-1521 Budapest, Hungary
基金
匈牙利科学研究基金会;
关键词
calibration; deconvolution; ill-posed problem; inverse problems; jitter;
D O I
10.1109/19.746557
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Time domain measurements are distorted by the measurement system if the bandwidth of the system is not sufficiently high compared to that of the signal to be measured. If the distortion is known the measured signal can be compensated for it (inverse filtering or deconvolution), Since the measurement is always corrupted by noise, the reconstruction is an estimation task, i.e., the reconstructed signal may vary depending on the actual noise record. Our aim is to investigate the errors related to the signal reconstruction, and to provide an error bound around the reconstructed time domain waveform. Based on their nature rye can distinguish between systematic and stochastic errors. In this paper, we investigate the stochastic type of errors and suggest a method to calculate the uncertainty (variance) of the reconstruction. We developed a method for the calibration of high-speed sampling systems. Both stationary and jitter noises will be investigated.
引用
收藏
页码:1062 / 1066
页数:5
相关论文
共 50 条
  • [1] Time domain uncertainty bound of signal reconstruction in the case of jittery and noisy measurements
    Daboczi, T
    [J]. WHERE INSTRUMENTATION IS GOING - CONFERENCE PROCEEDINGS, VOLS 1 AND 2, 1998, : 1296 - 1301
  • [2] Noncausal sampled signal reconstruction from noisy measurements: A system theoretic approach
    Meinsma, Gjerrit
    Mirkin, Leonid
    [J]. PROCEEDINGS OF THE 46TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-14, 2007, : 4943 - +
  • [3] Sparse signal reconstruction from noisy compressive measurements using cross validation
    Boufounos, Petros
    Duarte, Marco F.
    Baraniuk, Richard G.
    [J]. 2007 IEEE/SP 14TH WORKSHOP ON STATISTICAL SIGNAL PROCESSING, VOLS 1 AND 2, 2007, : 299 - 303
  • [4] Consistent Sampling and Signal Reconstruction in Noisy Under-Determined Case
    Hirabayashi, Akira
    [J]. IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES, 2012, E95A (03) : 631 - 638
  • [5] Sparse Signal Reconstruction from Quantized Noisy Measurements via GEM Hard Thresholding
    Qiu, Kun
    Dogandzic, Aleksandar
    [J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2012, 60 (05) : 2628 - 2634
  • [6] A review on uncertainty quantification of shadowing reconstruction and signal measurements in Radio Tomographic Imaging
    Tan, Jiaju
    Zhao, Qili
    Guo, Xuemei
    Zhao, Xin
    Wang, Guoli
    [J]. COMPUTER COMMUNICATIONS, 2022, 195 : 488 - 498
  • [7] On robust signal reconstruction in noisy filter banks
    Vikalo, H
    Hassibi, B
    Erdogan, AT
    Kailath, T
    [J]. SIGNAL PROCESSING, 2005, 85 (01) : 1 - 14
  • [8] Signal reconstruction from noisy multichannel samples
    Cheng, Dong
    Hu, Xiaoxiao
    Kou, Kit Ian
    [J]. DIGITAL SIGNAL PROCESSING, 2022, 129
  • [9] Iteratively Re-weighted Least Squares for Sparse Signal Reconstruction from Noisy Measurements
    Carrillo, Rafael E.
    Barner, Kenneth E.
    [J]. 2009 43RD ANNUAL CONFERENCE ON INFORMATION SCIENCES AND SYSTEMS, VOLS 1 AND 2, 2009, : 448 - 453
  • [10] Signal reconstruction from noisy random projections
    Haupt, Jarvis
    Nowak, Robert
    [J]. IEEE TRANSACTIONS ON INFORMATION THEORY, 2006, 52 (09) : 4036 - 4048