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Comparison of Compressed Sensing Based Algorithms for Sparse Signal Reconstruction

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Date
2016
Author
Çelik, Safa
Başaran, Mehmet
Erküçük, Serhat
Çırpan, Hakan Ali
Abstract
Compressed sensing theory shows that any signal which is defined as sparse in a given domain can be reconstructed using fewer linear projections instead of using all Nyquist-rate samples. In this paper we investigate basis pursuit matching pursuit orthogonal matching pursuit and compressive sampling matching pursuit algorithms which are basic compressed sensing based algorithms and present performance curves in terms of mean squared error for various parameters including signal-tonoise ratio sparsity and number of measurements with regard to mean squared error. In addition accuracy of estimation performances has been supported with theoretical lower bounds (Cramer-Rao lower bound and deterministic lower mean squared error). Considering estimation performances compressive sampling matching pursuit yields the best results unless the signal has a non-sparse structure.

Source

2016 24th Signal Processing And Communication Application Conference (SIU)

Pages

1441-1444

URI

https://hdl.handle.net/20.500.12469/517
https://doi.org/10.1109/SIU.2016.7496021

Collections

  • Araştırma Çıktıları / WOS [1518]
  • Elektrik-Elektronik Mühendisliği / Electrical - Electronics Engineering [321]

Keywords

Compressed Sensing
Greedy Methods
Cramer-Rao Lower Bound

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DSpace software copyright © 2002-2015  DuraSpace
Contact Us | Send Feedback
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