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RBF-network based sparse signal recovery algorithm for compressed sensing reconstruction.

L Vidya1, V Vivekanand1, U Shyamkumar1

  • 1Vikram Sarabhai Space Centre, ISRO, Thiruvananthapuram, India.

Neural Networks : the Official Journal of the International Neural Network Society
|December 16, 2014
PubMed
Summary
This summary is machine-generated.

This paper introduces a new Radial Basis Function-Least Square Error Projection Cascade Network for Sparse Signal Recovery (RASR) to enhance compressed sensing performance. The RASR algorithm improves signal recovery and computation time compared to existing methods.

Keywords:
Compressed SensingConvergence rateMeasurement matrixRadial basis function

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Area of Science:

  • Signal Processing
  • Machine Learning
  • Optimization

Background:

  • Compressed Sensing (CS) enables signal recovery from undersampled measurements.
  • Existing Artificial Neural Network (ANN) algorithms for CS recovery face challenges in computation time and convergence.
  • Sparse signal recovery is crucial in various applications like image processing and communications.

Purpose of the Study:

  • To analyze and improve existing ANN-based compressed sensing recovery algorithms.
  • To propose a novel cascaded network, RASR, for enhanced sparse signal recovery.
  • To evaluate the computational time and convergence of the proposed RASR algorithm.

Main Methods:

  • Development of a cascaded network combining Radial Basis Function (RBF) nodes and a Least Square Error (LSE) minimization block.
  • Utilization of smoothed L0 norm optimization, L2 LSE projection, and a feedback network model.
  • Comparison with existing algorithms like CSIANN and SL0 through simulations and experimental evaluations.

Main Results:

  • The proposed RASR algorithm demonstrates improved signal recovery performance.
  • RASR achieves a marginal reduction in computational time compared to the SL0 algorithm.
  • The cascaded network architecture effectively enhances the convergence and efficiency of sparse signal recovery.

Conclusions:

  • The RASR algorithm offers a promising approach for efficient and accurate sparse signal recovery in compressed sensing.
  • The integration of RBF nodes and LSE minimization in a cascaded network provides significant advantages.
  • Further research can explore variations and applications of this novel ANN-based recovery technique.