Sparse Representations for Radar with MATLAB Examples
Published on 6. November 2012
Book
Paperback/Softback
XIII, 71 pages
978-3-031-00391-2 (ISBN)
Description
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. Table of Contents: Radar Systems: A Signal Processing Perspective / Introduction to Sparse Representations / Dimensionality Reduction / Radar Signal Processing Fundamentals / Sparse Representations in Radar
More details
Series
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
XIII, 71 p.
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 6 mm
Weight
183 gr
ISBN-13
978-3-031-00391-2 (9783031003912)
DOI
10.1007/978-3-031-01519-9
Schweitzer Classification
Other editions
Additional editions
E-Book
05/2022
Springer
€28.88
Available for download
Person
Peter A. Knee received a B.S. (with honors) in electrical engineering from the University of New Mexico, Albuquerque, New Mexico, in 2006, and an M.S. degree in electrical engineering from Arizona State University in 2010. While at Arizona State University, his research included the analysis of high-dimensional Synthetic Aperture Radar (SAR) imagery for use with Automatic Target Recognition (ATR) systems as well as dictionary learning and data classification using sparse representations. He is currently an employee at Sandia National Laboratories in Albuquerque, New Mexico, focusing on SAR image analysis and software defined radios
Content
Radar Systems: A Signal Processing Perspective.- Introduction to Sparse Representations.- Dimensionality Reduction.- Radar Signal Processing Fundamentals.- Sparse Representations in Radar.