Sparse representations for radar with MATLAB examples
By: Knee, Peter.
Material type:
BookSeries: Synthesis digital library of engineering and computer science: ; Synthesis lectures on algorithms and software in engineering: # 10.Publisher: San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, c2012Description: 1 electronic text (xiii, 71 p.) : ill., digital file.ISBN: 9781627050357 (electronic bk.).Subject(s): MATLAB | Radar -- Mathematical models | Signal processing -- Mathematical models | radar | sparse representations | compressive sensing | MATLABDDC classification: 621.3848 Online resources: Abstract with links to resource Also available in print.| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
E books
|
PK Kelkar Library, IIT Kanpur | Available | EBKE447 |
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
Part of: Synthesis digital library of engineering and computer science.
Series from website.
Includes bibliographical references (p. 63-69).
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.
Abstract freely available; full-text restricted to subscribers or individual document purchasers.
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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.
Also available in print.
Title from PDF t.p. (viewed on November 24, 2012).
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