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Signal Reconstruction in Sensor Arrays Using Temporal-Spatial Sparsity Regularization

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Independent Component Analysis and Blind Signal Separation (ICA 2004)

Part of the book series: Lecture Notes in Computer Science ((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.

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References

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Author information

Authors and Affiliations

  1. Electrical Engineering Department, Technion – Israel Institute of Technology, Haifa, Israel

    Dmitri Model & Michael Zibulevsky

Authors
  1. Dmitri Model
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  2. Michael Zibulevsky
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Editor information

Editors and Affiliations

  1. Dept. of Architecture and Computer Technology, University of Granada, Spain

    Carlos G. Puntonet  & Alberto Prieto  & 

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© 2004 Springer-Verlag Berlin Heidelberg

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Model, D., Zibulevsky, M. (2004). Signal Reconstruction in Sensor Arrays Using Temporal-Spatial Sparsity Regularization. In: Puntonet, C.G., Prieto, A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30110-3_41

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  • DOI: https://doi.org/10.1007/978-3-540-30110-3_41

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23056-4

  • Online ISBN: 978-3-540-30110-3

  • eBook Packages: Springer Book Archive

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