Title Sparse representations for radar with MATLAB® examples / Peter Knee

Published Cham, Switzerland : Springer, ©2012

Online access available from: Synthesis Digital Library    View Resource Record

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Description 1 online resource (xiii, 71 pages) : illustrations Series Synthesis lectures on algorithms and software in engineering, 1938-1735 ; #10 Synthesis lectures on algorithms and software in engineering ; #10. 1938-1727 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 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 Analysis radar sparse representations compressive sensing MATLAB Notes Part of: Synthesis digital library of engineering and computer science Title from PDF title page (viewed on November 24, 2012) Bibliography Includes bibliographical references (pages 63-69) SUBJECT MATLAB. http://id.loc.gov/authorities/names/n92036881 MATLAB fast Subject Radar -- Mathematical models Signal processing -- Mathematical models TECHNOLOGY & ENGINEERING -- Radar. Radar -- Mathematical models Signal processing -- Mathematical models Form Electronic book ISBN 9781627050357 1627050353 1627050345 9781627050340 9783031015199 3031015193

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