Skip to main content

Sparsity-Based Multipath Exploitation

  • Chapter
  • First Online:
  • 462 Accesses

Part of the book series: Springer Theses ((Springer Theses))

Abstract

This chapter focuses on sparse reconstruction of targets in an indoor environment. Due to the front wall and surrounding scatterers, multipath propagation arises which is exploited to improve reconstruction results. The sparsity of the scene and the structure therein is leveraged to obtain a clean image from few measurements. Throughout this chapter, perfect knowledge of the room geometry is assumed and suppression of any wall or corner returns is required.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. F. Ahmad, M.G. Amin, Multi-location wideband synthetic aperture imaging for urban sensing applications. J. Franklin Inst. 345(6), 618–639 (2008)

    Article  MATH  Google Scholar 

  2. F. Ahmad, M.G. Amin, Wall clutter mitigation for MIMO radar configurations in urban sensing, in 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA), Montreal, Canada, pp. 1165–1170, July 2012

    Google Scholar 

  3. F. Ahmad, M.G. Amin, S. Kassam, A beamforming approach to stepped-frequency synthetic aperture through-the-wall radar imaging, in IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Puerto Vallarta, Mexico, pp. 24–27, December 2005

    Google Scholar 

  4. F. Ahmad, G. Frazer, S. Kassam, M.G. Amin, Design and implementation of near-field, wideband synthetic aperture beamformers. IEEE Trans. Aerosp. Electron. Syst. 40(1), 206–220 (2004)

    Article  Google Scholar 

  5. R. Baraniuk, V. Cevher, M. Duarte, C. Hegde, Model-based compressive sensing. IEEE Trans. Inf. Theory 56, 1982–2001 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  6. G. Beylkin, On the fast Fourier transform of functions with singularities. Appl. Comput. Harmonic Anal. 2(4), 363–381 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  7. V. Dang, O. Kilic, Joint DoA-range-doppler tracking of moving targets based on compressive sensing, in IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, pp. 141–142, Memphis, TN (2014)

    Google Scholar 

  8. T. Dogaru, C. Le, SAR images of rooms and buildings based on FDTD computer models. IEEE Trans. Geosci. Remote Sens. 47(5), 1388–1401 (2009)

    Article  Google Scholar 

  9. A. Dutt, V. Rokhlin, Fast Fourier transforms for nonequispaced data, II. Appl. Comput. Harmonic Anal. 2(1), 85–100 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  10. W. Deng, W. Yin, Y. Zhang, Group sparse optimization by alternating direction method, Department of Computational and Applied Mathematics, Rice University, Technical Report TR11-06 (2011)

    Google Scholar 

  11. Y.C. Eldar, P. Kuppinger, H. Bolcskei, Block-sparse signals: uncertainty relations and efficient recovery. IEEE Trans. Signal Process. 58(6), 3042–3054 (2010)

    Article  MathSciNet  Google Scholar 

  12. M. Leigsnering, F. Ahmad, M.G. Amin, A. Zoubir, Compressive sensing based specular multipath exploitation for through-the-wall radar imaging, in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, pp. 6004–6008, May 2013

    Google Scholar 

  13. M. Leigsnering, F. Ahmad, M.G. Amin, A. Zoubir, Multipath exploitation in through-the-wall radar imaging using sparse reconstruction. IEEE Trans. Aerosp. Electron. Syst. 50(2), 920–939 (2014)

    Article  Google Scholar 

  14. M. Leigsnering, F. Ahmad, M.G. Amin, A. Zoubir, Specular multipath exploitation for improved velocity estimation in through-the-wall radar imaging, in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Florence, Italy, pp. 1060–1064, May 2014

    Google Scholar 

  15. M. Leigsnering, M.G. Amin, F. Ahmad, A.M. Zoubir, Multipath exploitation and suppression for SAR imaging of building interiors: an overview of recent advances. IEEE Signal Process. Mag. 31(4), 110–119 (2014)

    Article  Google Scholar 

  16. M. Leigsnering, F. Ahmad, M.G. Amin, A. Zoubir, Compressive sensing based multipath exploitation for stationary and moving indoor target localization. IEEE J. Selected Topics Signal Process. 9(8), 1469–1483 (2015)

    Article  Google Scholar 

  17. M. Leigsnering, F. Ahmad, M.G. Amin, A. Zoubir, Multipath-aware velocity estimation for sparsity-based through-the-wall radar imaging, in IEEE International Radar Conference, Arlington, VA (2015)

    Google Scholar 

  18. M. Leigsnering, F. Ahmad, M.G. Amin, A. Zoubir, Multipath exploitation in sparse scene recovery using sensing-through-wall distributed radar sensor configurations, in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Brisbane, Australia, April 2015

    Google Scholar 

  19. M. Leigsnering, F. Ahmad, M.G. Amin, A. Zoubir, Parametric dictionary learning for sparsity-based TWRI in multipath environments. IEEE Trans. Aerosp. Electron. Syst. 52(2), 532–547 (2016)

    Article  Google Scholar 

  20. M. Leigsnering, A. Zoubir, Fast wideband near-field imaging using the non-equispaced FFT with application to through-wall radar, in European Signal Processing Conference (EUSIPCO), pp. 1708–1712, Spain, Barcelona (2011)

    Google Scholar 

  21. M. Leigsnering, A.M. Zoubir, Compressive sensing for urban multipath exploitation, in Compressive Sensing for Urban Radar (CRC Press, Boca Raton, 2014), pp. 153–196

    Google Scholar 

  22. J. Moulton, S. Kassam, F. Ahmad, M.G. Amin, K. Yemelyanov, Target and change detection in synthetic aperture radar sensing of urban structures, in IEEE Radar Conference (RADAR), Rome, Italy (2008)

    Google Scholar 

  23. D. Potts, G. Steidl, M. Tasche, Fast Fourier transforms for nonequispaced data: a tutorial. Birkhäuser, pp. 247–269 (2001). chap. 12

    Google Scholar 

  24. O. Pele, M. Werman, Fast and robust earth mover’s distances, in IEEE 12th International Conference on Computer Vision, Kyoto, Japan, pp. 460–467, September 2009

    Google Scholar 

  25. J. Qian, F. Ahmad, M.G. Amin, Joint localization of stationary and moving targets behind walls using sparse scene recovery. J. Electron. Imaging 22(2), 021002 (2013)

    Article  Google Scholar 

  26. M.A. Richards, J.A. Scheer, W.A. Holm (eds.), Principles of Modern Radar: Basic Principles (SciTech Publishing, Raleigh, NC, 2010)

    Google Scholar 

  27. Y. Rubner, C. Tomasi, L.J. Guibas, The earth mover’s distance as a metric for image retrieval. Int. J. Comput. Vision 40(2), 99–121 (2000)

    Article  MATH  Google Scholar 

  28. P. Setlur, M.G. Amin, F. Ahmad, Multipath model and exploitation in through-the-wall and urban radar sensing. IEEE Trans. Geosci. Remote Sens. 49(10), 4021–4034 (2011)

    Article  Google Scholar 

  29. P. Setlur, G. Alli, L. Nuzzo, Multipath exploitation in through-wall radar imaging via point spread functions. IEEE Trans. Image Process. 22(12), 4571–4586 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  30. M. Soumekh, Synthetic Aperture Radar Signal Processing with MATLAB Algorithms (Wiley, New York, 1999)

    MATH  Google Scholar 

  31. S. Wright, R. Nowak, M. Figueiredo, Sparse reconstruction by separable approximation. IEEE Trans. Signal Process. 57(7), 2479–2493 (2009)

    Article  MathSciNet  Google Scholar 

  32. Y.-S. Yoon, M. Amin, Compressed sensing technique for high-resolution radar imaging, in Proceedings of SPIE Signal Processing, Sensor Fusion, and Target Recognition XVII, vol. 6968, no. 1, Orlando, FL, p. 69681A, March 2008

    Google Scholar 

  33. M. Yuan, Y. Lin, Model selection and estimation in regression with grouped variables. J. Roy. Stat. Soc. B 68(1), 49–67 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  34. L. Yuan, J. Liu, J. Ye, Efficient methods for overlapping group lasso. IEEE Trans. Pattern Anal. Mach. Intell. 35(9), 2104–2116 (2013)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Michael Leigsnering .

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Leigsnering, M. (2018). Sparsity-Based Multipath Exploitation. In: Sparsity-Based Multipath Exploitation for Through-the-Wall Radar Imaging. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-74283-0_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-74283-0_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74282-3

  • Online ISBN: 978-3-319-74283-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics