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Estimation of Time-Varying Sparse Signals in Sensor Networks

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Compressed Sensing & Sparse Filtering

Part of the book series: Signals and Communication Technology ((SCT))

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Abstract

In this chapter, we consider the problem of reconstructing time-varying sparse signals in a sensor network with limited communication resources. In each time interval, the fusion center transmits the predicted signal estimate and its corresponding error covariance to a selected subset of sensors. The selected sensors compute quantized innovations and transmit them to the fusion center. We consider the situation where the signal is sparse, i.e., a large fraction of its components is zero-valued. We discuss algorithms for signal estimation in the described scenario, analyze their complexity, and demonstrate their near-optimal performance even in the case where sensors transmit a single bit (i.e., the sign of innovation) to the fusion center.

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Author information

Authors and Affiliations

  1. University of Texas, Austin, TX, USA

    Manohar Shamaiah & Haris Vikalo

Authors
  1. Manohar Shamaiah
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  2. Haris Vikalo
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Corresponding author

Correspondence to Manohar Shamaiah .

Editor information

Editors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, Nanyang Technical University, Singapore, Singapore

    Avishy Y. Carmi

  2. School of Computing and Communications, Lancaster University, Lancaster, United Kingdom

    Lyudmila Mihaylova

  3. Department of Engineering, University of Cambridge, Cambridge, United Kingdom

    Simon J. Godsill

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Shamaiah, M., Vikalo, H. (2014). Estimation of Time-Varying Sparse Signals in Sensor Networks. In: Carmi, A., Mihaylova, L., Godsill, S. (eds) Compressed Sensing & Sparse Filtering. Signals and Communication Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38398-4_12

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  • DOI: https://doi.org/10.1007/978-3-642-38398-4_12

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38397-7

  • Online ISBN: 978-3-642-38398-4

  • eBook Packages: EngineeringEngineering (R0)

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