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The Resource Sparse representations for radar with MATLAB examples, Peter Knee, (electronic book)

Sparse representations for radar with MATLAB examples, Peter Knee, (electronic book)

Label
Sparse representations for radar with MATLAB examples
Title
Sparse representations for radar with MATLAB examples
Statement of responsibility
Peter Knee
Creator
Subject
Language
eng
Summary
Although the field of sparse representations is relatively new, research activities in academic and industrial research labs are already producing encouraging results. The sparse signal or parameter model motivated several researchers and practitioners to explore high complexity/wide bandwidth applications such as Digital TV, MRI processing, and certain defense applications. The potential signal processing advancements in this area may influence radar technologies. This book presents the basic mathematical concepts along with a number of useful MATLAB examples to emphasize the practical implementations both inside and outside the radar field
Member of
Cataloging source
CaBNVSL
http://library.link/vocab/creatorName
Knee, Peter.
Dewey number
621.3848
Illustrations
illustrations
Index
no index present
LC call number
TK6578
LC item number
.K547 2012
Literary form
non fiction
Nature of contents
  • abstracts summaries
  • bibliography
http://library.link/vocab/subjectName
  • Radar
  • Signal processing
Target audience
specialized
Label
Sparse representations for radar with MATLAB examples, Peter Knee, (electronic book)
Instantiates
Publication
Bibliography note
Includes bibliographical references (p. 63-69)
Color
multicolored
Contents
  • List of symbols -- List of acronyms -- Acknowledgments --
  • 1. Radar systems: a signal processing perspective -- 1.1 History of radar -- 1.2 Current radar applications -- 1.3 Basic organization --
  • 2. Introduction to sparse representations -- 2.1 Signal coding using sparse representations -- 2.2 Geometric interpretation -- 2.3 Sparse recovery algorithms -- 2.3.1 Convex optimization -- 2.3.2 Greedy approach -- 2.4 Examples -- 2.4.1 Non-uniform sampling -- 2.4.2 Image reconstruction from Fourier sampling --
  • 3. Dimensionality reduction -- 3.1 Linear dimensionality reduction techniques -- 3.1.1 Principal component analysis (PCA) and multidimensional scaling (MDS) -- 3.1.2 Linear discriminant analysis (LDA) -- 3.2 Nonlinear dimensionality reduction techniques -- 3.2.1 ISOMAP -- 3.2.2 Local linear embedding (LLE) -- 3.2.3 Linear model alignment -- 3.3 Random projections --
  • 4. Radar signal processing fundamentals -- 4.1 Elements of a pulsed radar -- 4.2 Range and angular resolution -- 4.3 Imaging -- 4.4 Detection --
  • 5. Sparse representations in radar -- 5.1 Echo signal detection and image formation -- 5.2 Angle-Doppler-range estimation -- 5.3 Image registration (matching) and change detection for SAR -- 5.4 Automatic target classification -- 5.4.1 Sparse representation for target classification -- 5.4.2 Sparse representation-based spatial pyramids --
  • A. Code sample -- Non-uniform sampling and signal reconstruction code -- Long-Shepp phantom test image reconstruction code -- Signal bandwidth code -- Bibliography -- Author's biography
Control code
201208ASE010
Dimensions
unknown
Extent
1 electronic text (xiii, 71 p.)
File format
multiple file formats
Form of item
online
Isbn
9781627050357
Issn
1938-1735
Other physical details
ill., digital file.
Reformatting quality
access
Specific material designation
remote
Label
Sparse representations for radar with MATLAB examples, Peter Knee, (electronic book)
Publication
Bibliography note
Includes bibliographical references (p. 63-69)
Color
multicolored
Contents
  • List of symbols -- List of acronyms -- Acknowledgments --
  • 1. Radar systems: a signal processing perspective -- 1.1 History of radar -- 1.2 Current radar applications -- 1.3 Basic organization --
  • 2. Introduction to sparse representations -- 2.1 Signal coding using sparse representations -- 2.2 Geometric interpretation -- 2.3 Sparse recovery algorithms -- 2.3.1 Convex optimization -- 2.3.2 Greedy approach -- 2.4 Examples -- 2.4.1 Non-uniform sampling -- 2.4.2 Image reconstruction from Fourier sampling --
  • 3. Dimensionality reduction -- 3.1 Linear dimensionality reduction techniques -- 3.1.1 Principal component analysis (PCA) and multidimensional scaling (MDS) -- 3.1.2 Linear discriminant analysis (LDA) -- 3.2 Nonlinear dimensionality reduction techniques -- 3.2.1 ISOMAP -- 3.2.2 Local linear embedding (LLE) -- 3.2.3 Linear model alignment -- 3.3 Random projections --
  • 4. Radar signal processing fundamentals -- 4.1 Elements of a pulsed radar -- 4.2 Range and angular resolution -- 4.3 Imaging -- 4.4 Detection --
  • 5. Sparse representations in radar -- 5.1 Echo signal detection and image formation -- 5.2 Angle-Doppler-range estimation -- 5.3 Image registration (matching) and change detection for SAR -- 5.4 Automatic target classification -- 5.4.1 Sparse representation for target classification -- 5.4.2 Sparse representation-based spatial pyramids --
  • A. Code sample -- Non-uniform sampling and signal reconstruction code -- Long-Shepp phantom test image reconstruction code -- Signal bandwidth code -- Bibliography -- Author's biography
Control code
201208ASE010
Dimensions
unknown
Extent
1 electronic text (xiii, 71 p.)
File format
multiple file formats
Form of item
online
Isbn
9781627050357
Issn
1938-1735
Other physical details
ill., digital file.
Reformatting quality
access
Specific material designation
remote

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