Authors:
Fundamental contributions to multistatic imaging
New dictionary-matching techniques for imaging
Matlab codes for the main algorithms described in the book are provided
Includes supplementary material: sn.pub/extras
Part of the book series: Lecture Notes in Mathematics (LNM, volume 2098)
Buying options
This is a preview of subscription content, access via your institution.
Table of contents (18 chapters)
-
Front Matter
-
Mathematical and Probabilistic Tools
-
Front Matter
-
-
Small Volume Expansions and Concept of Generalized Polarization Tensors
-
Front Matter
-
-
Multistatic Configuration
-
Front Matter
-
-
Localization and Detection Algorithms
-
Front Matter
-
-
Dictionary Matching and Tracking Algorithms
-
Front Matter
-
-
Imaging of Extended Targets
-
Front Matter
-
About this book
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.
Keywords
- 35R30,35B30
- Detection, localization, and characterization
- Dictionary matching algorithms
- Mathematical imaging
- Optimal control algorithms
- Shape representations
Authors and Affiliations
-
Department of Mathematics and Applications, École Normale Supérieure, Paris, France
Habib Ammari, Wenjia Jing, Han Wang
-
Laboratory of Probability and Random Mod, University Paris VII, Paris, France
Josselin Garnier
-
Department of Mathematics, Inha University, Incheon, Korea, Republic of (South Korea)
Hyeonbae Kang
-
Department of Mathematical Sciences, Korean Advanced Institute of Science and Technology (KASIT), Daejeon, Korea, Republic of (South Korea)
Mikyoung Lim
-
Dept. Mathematics, University of California, Irvine School of Physical Sciences, Irvine, USA
Knut Sølna
Bibliographic Information
Book Title: Mathematical and Statistical Methods for Multistatic Imaging
Authors: Habib Ammari, Josselin Garnier, Wenjia Jing, Hyeonbae Kang, Mikyoung Lim, Knut Sølna, Han Wang
Series Title: Lecture Notes in Mathematics
DOI: https://doi.org/10.1007/978-3-319-02585-8
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer International Publishing Switzerland 2013
Softcover ISBN: 978-3-319-02584-1Published: 17 December 2013
eBook ISBN: 978-3-319-02585-8Published: 29 November 2013
Series ISSN: 0075-8434
Series E-ISSN: 1617-9692
Edition Number: 1
Number of Pages: XVII, 361
Number of Illustrations: 14 b/w illustrations, 47 illustrations in colour
Topics: Mathematical Physics