Amin Compressive Sensing for Urban Radar

1. Auflage 2014
ISBN: 978-1-4665-9785-3
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 508 Seiten

With the emergence of compressive sensing and sparse signal reconstruction, approaches to urban radar have shifted toward relaxed constraints on signal sampling schemes in time and space, and to effectively address logistic difficulties in data acquisition. Traditionally, these challenges have hindered high resolution imaging by restricting both bandwidth and aperture, and by imposing uniformity and bounds on sampling rates.

Compressive Sensing for Urban Radar is the first book to focus on a hybrid of two key areas: compressive sensing and urban sensing. It explains how reliable imaging, tracking, and localization of indoor targets can be achieved using compressed observations that amount to a tiny percentage of the entire data volume. Capturing the latest and most important advances in the field, this state-of-the-art text:

- Covers both ground-based and airborne synthetic aperture radar (SAR) and uses different signal waveforms

- Demonstrates successful applications of compressive sensing for target detection and revealing building interiors

- Describes problems facing urban radar and highlights sparse reconstruction techniques applicable to urban environments

- Deals with both stationary and moving indoor targets in the presence of wall clutter and multipath exploitation

- Provides numerous supporting examples using real data and computational electromagnetic modeling

Featuring 13 chapters written by leading researchers and experts, Compressive Sensing for Urban Radar is a useful and authoritative reference for radar engineers and defense contractors, as well as a seminal work for graduate students and academia.

Zielgruppe

Radar engineers, defense contractors, and researchers as well as graduate students and academia.

Autoren/Hrsg.

Amin, Moeness

Fachgebiete

• Geowissenschaften Geologie GIS, Geoinformatik
• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Mikrowellentechnik

Inhaltsverzeichnis

Compressive Sensing Fundamentals
Michael B. Wakin
Colorado School of Mines, Golden, USA

Overcomplete Dictionary Design for Sparse Reconstruction of Building Layout Mapping
Wim van Rossum and Jacco de Wit
Netherlands Organization for Applied Scientific Research (TNO), The Hague

Compressive Sensing for Radar Imaging of Underground Targets
Kyle R. Krueger, James H. McClellan, and Waymond R. Scott, Jr.
Georgia Institute of Technology, Atlanta, USA

Wall Clutter Mitigations for Compressive Imaging of Building Interiors
Fauzia Ahmad
Villanova University, Pennsylvania, USA

Compressive Sensing for Urban Multipath Exploitation
Michael Leigsnering and Abdelhak M. Zoubir
Darmstadt University of Technology, Germany

Compressive Sensing Kernel Design for Imaging of Urban Objects
Nathan A. Goodman, Junhyeong Bae, and Yujie Gu
The University of Oklahoma, Norman, USA

Compressive Sensing for Multi-Polarization Through-Wall Radar Imaging
Abdesselam Bouzerdoum, Jack Yang, and Fok Hing Chi Tivive
University of Wollongong, New South Wales, Australia

Sparsity-Aware Human Motion Indication
Moeness G. Amin
Villanova University, Pennsylvania, USA

Time-Frequency Analysis of Micro-Doppler Signals based on Compressive Sensing
Ljubisa Stankovic, Srdjan Stankovic, Irena Orovic, and Yimin D. Zhang
University of Montenegro, Podgorica and Villanova University, Pennsylvania, USA

Urban Target Tracking using Sparse Representations
Phani Chavali and Arye Nehorai
Washington University in St. Louis, Missouri, USA

3D Imaging of Vehicles from Sparse Apertures in Urban Environment
Emre Ertin
The Ohio State University, Columbus, USA

Compressive Sensing for MIMO Urban Radar
Yao Yu and Athina Petropulu
San Diego, California, USA and Rutgers, The State University of New Jersey, Piscataway, USA

Compressive Sensing Meets Noise Radar
Mahesh C. Shastry, Ram M. Narayanan, and Muralidhar Rangaswamy
The Pennsylvania State University, State College, USA and Air Force Research Laboratory, Wright-Patterson Air Force Base, Ohio, USA

Über Autor(innen)

Dr. Moeness G. Amin has been a faculty member of the Department of Electrical and Computer Engineering at Villanova University, Pennsylvania, USA for nearly 30 years. In 2002, he became the director of the Center for Advanced Communications, College of Engineering. Currently he is the chair of the Electrical Cluster of the Franklin Institute Committee on Science and the Arts, as well as an IEEE, SPIE, and IET fellow. The recipient of many prestigious awards, he has conducted extensive research in radar signal processing, authored over 650 journal and conference papers, and served as an editor for numerous publications.