Looking through Walls - Compressive sensing for urban radar

COMPRESSIVE SENSING FOR URBAN RADAR - Plenary_Amin.pdf

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Moeness Amin
Radar 2014 - Keynotes

Abstract : Sparsity-Aware Urban Radar
Compressive Sensing for Urban Radars, or Compressive Urban Radar (CUR), is an area of research and development which investigates the radar performance within the context of compressive sensing and with a focus on urban applications. CUR examines the effect of using significantly reduced data measurements in time, space and frequency on 2D and 3D imaging quality, strong EM reflections from exterior and interior walls, target multipath and ghosts, and moving target detection and tracking. In this respect, CUR is a hybrid between the two areas of compressive sensing and urban sensing. In essence, it enables reliable localization and imaging of indoor targets using a very small percentage of the entire data volume. Reduced or compressed observations can be due logistical difficulty in data collection or motivated by the need for fast data acquisition.

In this talk, compressive sensing will be put in context for radar, in general, and in particular for the urban environment. We will explain how CS can achieve various radar sensing goals and objectives, and how it compares with the use of full data volume. Different radar specifications and configurations will be used. In particular, we will address CS for urban radars towards achieving

• (a) Imaging through walls;
• (b) Detection of behind the wall targets;
• (c) Mitigation of wall clutter; and
• (d) Exploitation of multipath. 

All of the above issues will be examined using data generated at the Radar Imaging Lab, Villanova University.

UC San Diego /All Collec

Compressive Sensing for Urban Radar - CRC Press Book

Title	Compressive sensing for urban radar / edited by Moeness Amin
Published	Boca Raton : CRC Press, [2014]
Copyright	©2015

"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"-- Provided by  publisher

Other Author	Amin, Moeness G. editor of compilation
Other Title	CRC Press. ENGnetBASE online monographs DDA 2014
	CRC Press. ENVIROnetBASE online monographs 2013-
	CRC Press. EnvironmentalSciencenetBASE online monographs 2013-
ISBN	9781466597846 (hardback)
	1466597844 (hardback)
	9781466597853
	An Introduction To Compressive Sampling.pdf 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