Skip to main content
SpringerLink
Log in

Aggregating descriptive regularization and Bayesian nonparametric spectral estimation approaches for enhanced radar imaging

  • Published:
Radioelectronics and Communications Systems Aims and scope Submit manuscript

Abstract

In this paper, we address and discuss a novel look at the high-resolution array radar/SAR imaging as an ill-conditioned inverse spatial spectrum pattern (SSP) estimation problem. The system-oriented theoretical developments are addressed to as an aggregated descriptive regularization-Bayesian (DRB) method for radar/SAR image formation/reconstruction. We exemplify how this aggregated method leads to new robust adaptive computational techniques that enable one to derive efficient and consistent estimates of the SSP via unifying the Bayesian minimum risk nonparametric spectral estimation strategy with the maximum entropy randomized a priori image model and other projection-type regularization constraints imposed on the solution. The reported simulation results demonstrate the efficiency of the addressed DRB-related radar/SAR-oriented enhanced imaging techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Ya. S. Shifrin, Statistical Antenna Theory (Golem Press, 1971).

  2. L. G. Cutrona, “Synthetic aperture radar”, Radar Handbook, 2nd ed. (McGraw Hill, New York, 1990) [ed. by Skolnik].

    Google Scholar 

  3. F. M. Henderson and A. V. Lewis, Eds., Principles and Applications of Imaging Radar, Manual of Remote Sensing, Vol. 3, 3rd ed. (Willey, New York, 1998).

    Google Scholar 

  4. Yu.V. Shkvarko, “Estimation of wavefield power distribution in the remotely sensed environment: Bayesian maximum entropy approach,” IEEE Trans. Signal Process. 50, No. 9, 2333 (Sep. 2002).

    Article  MathSciNet  Google Scholar 

  5. Yu. V. Shkvarko, “Unifying regularization and Bayesian estimation methods for enhanced imaging with remotely sensed data. Part I: Theory,” IEEE Trans. Geosci. Remote Sensing, No. 3, 923 (March 2004).

  6. Yu. V. Shkvarko, “Unifying regularization and Bayesian estimation methods for enhanced imaging with remotely sensed data. Part II: Implementation and performance issues,” IEEE Trans. Geosci. Remote Sensing 42, No. 3, 932 (March 2004).

    Article  Google Scholar 

  7. Yu. V. Shkvarko, “Unifying experiment design and convex regularization techniques for enhanced imaging with uncertain remote sensing data. Part I: Theory,” IEEE Trans. Geosci. Remote Sensing 48, No. 1, 82 (Jan. 2010).

    Article  Google Scholar 

  8. Yu. V. Shkvarko, “Unifying experiment design and convex regularization techniques for enhanced imaging with uncertain remote sensing data. Part II: Adaptive implementation and performance issues,” IEEE Trans. Geosci. Remote Sensing 48, No. 1, 96 (Jan. 2010).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CINVESTAV del IPN, Unidad Guadalajara, Mexico, Mexico

    Yu. V. Shkvarko

Authors
  1. Yu. V. Shkvarko
    View author publications

    You can also search for this author in PubMed Google Scholar

Additional information

Original Russian Text © Yu.V. Shkvarko, 2010, published in Izv. Vyssh. Uchebn. Zaved., Radioelektron., 2010, Vol. 53, No. 4, pp. 37–43.

About this article

Cite this article

Shkvarko, Y.V. Aggregating descriptive regularization and Bayesian nonparametric spectral estimation approaches for enhanced radar imaging. Radioelectron.Commun.Syst. 53, 203–207 (2010). https://doi.org/10.3103/S0735272710040047

Download citation

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0735272710040047

Keywords

Search

Navigation