Sparse Representations for Radar with MATLAB Examples

Erscheinungsjahr 2012
ISBN: 978-3-031-00391-2
Verlag: Springer International Publishing

Buch, Englisch, 71 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 183 g

Reihe: Synthesis Lectures on Algorithms and Software in Engineering

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

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Knee, Peter

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung

Inhaltsverzeichnis

Radar Systems: A Signal Processing Perspective.- Introduction to Sparse Representations.- Dimensionality Reduction.- Radar Signal Processing Fundamentals.- Sparse Representations in Radar.

Über Autor(innen)

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