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)


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.


Sensor Array Wavelet Packet Independent Component Analysis Blind Source Separation Precondition Conjugate Gradient 
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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

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