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Autor(en): 
  • Habib Ammari
  • Hyeonbae Kang
  • Han Wang
  • Josselin Garnier
  • Wenjia Jing
  • Mikyoung Lim
  • Knut Sølna
  • Mathematical and Statistical Methods for Multistatic Imaging 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 7-14 Tagen versandfertig
    Veröffentlichung:  Dezember 2013  
    Genre:  Naturwissensch., Medizin, Technik 
     
    Applications of Mathematics / B / Mathematical Applications in the Physical Sciences / Mathematical physics / Mathematics and Statistics
    ISBN:  9783319025841 
    EAN-Code: 
    9783319025841 
    Verlag:  Springer EN 
    Einband:  Kartoniert  
    Sprache:  English  
    Serie:  #2098 - Lecture Notes in Mathematics  
    Dimensionen:  H 235 mm / B 155 mm / D  
    Gewicht:  5796 gr 
    Seiten:  378 
    Illustration:  XVII, 361 p. 61 illus., 47 illus. in color., farbige Illustrationen, schwarz-weiss Illustrationen 
    Zus. Info:  EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025. 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    This book covers recent mathematical, numerical, and statistical approaches for multistatic imaging of targets with waves at single or multiple frequencies. The waves can be acoustic, elastic or electromagnetic. They are generated by point sources on a transmitter array and measured on a receiver array. An important problem in multistatic imaging is to quantify and understand the trade-offs between data size, computational complexity, signal-to-noise ratio, and resolution. Another fundamental problem is to have a shape representation well suited to solving target imaging problems from multistatic data. In this book the trade-off between resolution and stability when the data are noisy is addressed. Efficient imaging algorithms are provided and their resolution and stability with respect to noise in the measurements analyzed. It also shows that high-order polarization tensors provide an accurate representation of the target. Moreover, a dictionary-matching technique based on new invariants for the generalized polarization tensors is introduced. Matlab codes for the main algorithms described in this book are provided. Numerical illustrations using these codes in order to highlight the performance and show the limitations of numerical approaches for multistatic imaging are presented.

      



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