Advertisement

Signal Reconstruction in Sensor Arrays Using Temporal-Spatial Sparsity Regularization

  • Dmitri Model
  • Michael Zibulevsky
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3195)

Abstract

We propose a technique of multisensor signal reconstruction based on the assumption, that source signals are spatially sparse, as well as have sparse [wavelet-type] representation in time domain. This leads to a large scale convex optimization problem, which involves l 1 norm minimization. The optimization is carried by the Truncated Newton method, using preconditioned Conjugate Gradients in inner iterations. The byproduct of reconstruction is the estimation of source locations.

Keywords

Sensor Array Wavelet Packet Independent Component Analysis Blind Source Separation Precondition Conjugate Gradient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Zibulevsky, M., Pearlmutter, B.A.: Blind source separation by sparse decomposition in a signal dictionary. Neural Computations 13(4), 863–882 (2001)zbMATHCrossRefGoogle Scholar
  2. 2.
    Zibulevsky, M., Pearlmutter, B.A., Bofill, P., Kisilev, P.: Blind source separation by sparse decomposition. In: Roberts, S.J., Everson, R.M. (eds.) Independent Components Analysis: Princeiples and Practice, Cambridge University Press, Cambridge (2001)Google Scholar
  3. 3.
    Cȩtin, M., Malioutov, D.M., Willsky, A.S.: A variational technique for source localization based on a sparse signal reconstruction perspective. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, May 2002, vol. 3, pp. 2965–2968 (2002)Google Scholar
  4. 4.
    Malioutov, D.M., Cȩtin, M., FisherIII, J.W., Willsky, A.S.: Superresolution source localization through data-adaptive regularization. In: IEEE Sensor Array and Multichannel Signal Processing Workshop, August 2002, pp. 194–198 (2002)Google Scholar
  5. 5.
    Model, D., Zibulevsky, M.: Sparse multisensor signal reconstruction. CCIT report #467, EE department, Technion - Israel Institute of Technology (February 2004)Google Scholar
  6. 6.
    Chen, S.S., Donoho, D.L., Saunders, M.A.: Atomic decomposition by basis pursuit. SIAM J. Sci. Comput. 20(1), 33–61 (1998)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Dembo, R.S., Eisenstat, S.C., Steihaug, T.: Inexact newton methods. SIAM Journal on Numerical Analysis 19, 400–408 (1982)zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Nash, S.: A survey of truncated-newton methods. Journal of Computational and Applied Mathematics 124, 45–59 (2000)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Gill, P.E., Murray, W., Wright, M.H.: Practical Optimization. Academic Press, New York (1981)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Dmitri Model
    • 1
  • Michael Zibulevsky
    • 1
  1. 1.Electrical Engineering DepartmentTechnion – Israel Institute of TechnologyHaifaIsrael

Personalised recommendations