DETECTION, RECONSTRUCTION, AND CHARACTERIZATION ALGORITHMS FROM NOISY DATA IN MULTISTATIC WAVE IMAGING

被引:6
|
作者
Ammari, Habib [1 ]
Garnier, Josselin [2 ,3 ]
Jugnon, Vincent [4 ]
机构
[1] Ecole Normale Super, Dept Math & Applicat, F-75005 Paris, France
[2] Univ Paris Diderot, Lab Probabilites & Modeles Aleatoires, F-75205 Paris 13, France
[3] Univ Paris Diderot, Lab Jacques Louis Lions, F-75205 Paris 13, France
[4] MIT, Dept Math, Cambridge, MA 02139 USA
来源
DISCRETE AND CONTINUOUS DYNAMICAL SYSTEMS-SERIES S | 2015年 / 8卷 / 03期
关键词
Multistatic wave imaging; noisy data; detection and reconstruction algorithms; random matrices; measurement noise; Helmholtz equation; SCATTERING-AMPLITUDE; INVERSE SCATTERING; NUMERICAL-METHODS; SMALL INCLUSIONS; CONDUCTIVITY; MATRIX;
D O I
10.3934/dcdss.2015.8.389
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
The detection, localization, and characterization of a collection of targets embedded in a medium is an important problem in multistatic wave imaging. The responses between each pair of source and receiver are collected and assembled in the form of a response matrix, known as the multi-static response matrix. When the data are corrupted by measurement or instrument noise, the structure of the response matrix is studied by using random matrix theory. It is shown how the targets can be efficiently detected, localized and characterized. Both the case of a collection of point reflectors in which the singular vectors have all the same form and the case of small-volume electromagnetic inclusions in which the singular vectors may have different forms depending on their magnetic or dielectric type are addressed.
引用
收藏
页码:389 / 417
页数:29
相关论文
共 50 条
  • [1] Joint Network Reconstruction and Community Detection from Rich but Noisy Data
    Hu, Jie
    Chen, Xiao
    Chen, Yu
    Zhang, Weiping
    JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 2024, 33 (02) : 501 - 514
  • [2] Sparse approximations of phase and amplitude for wave field reconstruction from noisy data
    Katkovnik, Vladimir
    Shevkunov, Igor A.
    Petrov, Nikolay V.
    Egiazarian, Karen
    HOLOGRAPHY: ADVANCES AND MODERN TRENDS IV, 2015, 9508
  • [3] Tomographic reconstruction from noisy data
    Golan, A
    Dose, V
    BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING, 2002, 617 : 248 - 258
  • [4] SCATTERER RECONSTRUCTION FROM MULTISTATIC FAR-FIELD DATA
    MECKELBURG, HJ
    ELECTRONICS LETTERS, 1982, 18 (08) : 341 - 343
  • [5] Anatomically constrained reconstruction from noisy data
    Haldar, Justin P.
    Hernando, Diego
    Song, Sheng-Kwei
    Liang, Zhi-Pei
    MAGNETIC RESONANCE IN MEDICINE, 2008, 59 (04) : 810 - 818
  • [6] Reconstruction of gratings from noisy reflection data
    Skaar, J
    Feced, R
    JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION, 2002, 19 (11): : 2229 - 2237
  • [7] Metric Graph Reconstruction from Noisy Data
    Aanjaneya, Mridul
    Chazal, Frederic
    Chen, Daniel
    Glisse, Marc
    Guibas, Leonidas
    Morozov, Dmitriy
    COMPUTATIONAL GEOMETRY (SCG 11), 2011, : 37 - 46
  • [8] METRIC GRAPH RECONSTRUCTION FROM NOISY DATA
    Aanjaneya, Mridul
    Chazal, Frederic
    Chen, Daniel
    Glisse, Marc
    Guibas, Leonidas
    Morozov, Dmitriy
    INTERNATIONAL JOURNAL OF COMPUTATIONAL GEOMETRY & APPLICATIONS, 2012, 22 (04) : 305 - 325
  • [9] Real-time Radar Algorithms for Multistatic Millimetre-wave Imaging with Sparse Apertures
    Skouroliakou, Vasiliki
    Molaei, Amir Masoud
    Fusco, Vincent
    Yurduseven, Okan
    2022 23RD INTERNATIONAL RADAR SYMPOSIUM (IRS), 2022, : 390 - 395
  • [10] Time reversal imaging of obscured targets from multistatic data
    Devaney, AJ
    IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, 2005, 53 (05) : 1600 - 1610