Compressive Sensing for Urban Radar: 1st Edition (Paperback) book cover

Compressive Sensing for Urban Radar

1st Edition

Edited by Moeness Amin

CRC Press

508 pages | 196 B/W Illus.

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Description

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.

Reviews

"The essential feature of this book is that it brings together the areas of compressive sensing and radar imaging for urban sensing. These areas of attributes are highly relevant to promote sustainability and for a range of civil and military applications, such as search and rescue missions, hostage rescue situations, urban design, and surveillance and reconnaissance in urban environments."

—Fulvio Gini, University of Pisa, Italy

Table of Contents

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

About the Editor

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.

Subject Categories

BISAC Subject Codes/Headings:
TEC007000
TECHNOLOGY & ENGINEERING / Electrical
TEC024000
TECHNOLOGY & ENGINEERING / Microwaves
TEC036000
TECHNOLOGY & ENGINEERING / Remote Sensing & Geographic Information Systems