Fast Parametric ISAR Autofocus Algorithm Based on Entropy and Eigenvalue Decomposition

Document Type : Original Article

Author

Shiraz University

Abstract

In this paper, a fast-parametric method for ISAR autofocus is proposed which is based on the minimum entropy method and eigenvalue decomposition, and has less computational complexity than that of conventional autofocus methods. In this technique, the covariance matrix of the range compressed and aligned data is formed and by utilizing eigenvalue decomposition, signal and noise are separated. Then, the Fourier transform of the signal eigenvectors which are much smaller than the total eigenvectors is taken. Finally, by applying the conventional autofocus approaches to the image of eigenvectors, the phase error is estimated. In this paper, a parametric method based on entropy is utilized. The simulation results show that although the computational complexity is decreased, the performance of the algorithm is maintained.

Keywords

References

 
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Radar
Volume 7, Issue 1 - Serial Number 21
December 2019
Pages 15-23

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History

  • Receive Date: 04 April 2019
  • Revise Date: 29 June 2019
  • Accept Date: 29 June 2019
  • Publish Date: 23 August 2019

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