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Artificial Intelligence and Evolutionary Computations in Engineering Systems pp 471–480Cite as

Performance Improvement of Compressed Sensing Reconstruction Using Modified-AMP Algorithm

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 394))

Abstract

Compressed sensing (CS) is an emerging field which enables the undersampling of sparse signals rather than at the Nyquist rate. But the main computational challenge involved is in the reconstruction process as it is nonlinear in nature and the solution is obtained by solving a set of under determined linear equations. Greedy algorithms offer the solution to these kinds of problems with less computational complexity than the convex relaxations or linear programming methods. The approximate message passing algorithm offers accurate reconstruction of even the approximately sparse signals with reasonable computational intensity. In this paper, we have implemented a modified version of AMP algorithm and obtained a 50 % reduction in mean squared error and an improvement in signal-to-noise ratio.

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Author information

Authors and Affiliations

  1. Department of Electronics and Communication, Amrita Vishwa Vidyapeetham, Coimbatore, India

    Nissy Sara Mathai & R. Gandhiraj

Authors
  1. Nissy Sara Mathai
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  2. R. Gandhiraj
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Corresponding author

Correspondence to Nissy Sara Mathai .

Editor information

Editors and Affiliations

  1. Electrical and Electronics Engineer, SRM Engineering College, Kattankulathur, Tamil Nadu, India

    Subhransu Sekhar Dash

  2. Electrical & Electronics Engineering, Velammal Engineering College, Chennai, Tamil Nadu, India

    M. Arun Bhaskar

  3. Dept Electrical & Electronics Engg, IIT Delhi, New Delhi, India

    Bijaya Ketan Panigrahi

  4. Indian Statistical Institute, Kolkata, India

    Swagatam Das

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Mathai, N.S., Gandhiraj, R. (2016). Performance Improvement of Compressed Sensing Reconstruction Using Modified-AMP Algorithm. In: Dash, S., Bhaskar, M., Panigrahi, B., Das, S. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 394. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2656-7_43

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  • DOI: https://doi.org/10.1007/978-81-322-2656-7_43

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2654-3

  • Online ISBN: 978-81-322-2656-7

  • eBook Packages: EngineeringEngineering (R0)

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