Monday, September 15, 2014

Looking through Walls - Compressive sensing for urban radar


COMPRESSIVE SENSING FOR URBAN RADAR - Plenary_Amin.pdf


Moeness 133x200
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:
  1. Compressive Sensing Fundamentals, Michael B. Wakin, Colorado School of Mines, Golden, USA
  2. 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
  3. 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
  4. Wall Clutter Mitigations for Compressive Imaging of Building Interiors, Fauzia Ahmad, Villanova University, Pennsylvania, USA
  5. Compressive Sensing for Urban Multipath Exploitation, Michael Leigsnering and Abdelhak M. Zoubir, Darmstadt University of Technology, Germany
  6. Compressive Sensing Kernel Design for Imaging of Urban Objects, Nathan A. Goodman, Junhyeong Bae, and Yujie Gu, The University of Oklahoma, Norman, USA
  7. 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
  8. Sparsity-Aware Human Motion Indication, Moeness G. Amin, Villanova University, Pennsylvania, USA
  9. 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
  10. Urban Target Tracking using Sparse Representations
    Phani Chavali and Arye Nehorai
    Washington University in St. Louis, Missouri, USA
  11. 3D Imaging of Vehicles from Sparse Apertures in Urban Environment
    Emre Ertin
    The Ohio State University, Columbus, USA
  12. 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
  13. 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

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