The Covariance Matrix Analysis and Statistics for Near-field Sources Localization Model

Authors

  • František Nebus Armed Forces Academy of General M. R. Štefánik, Liptovský Mikuláš, Slovak Republic
  • Stanislava Gažovová Armed Forces Academy of General M. R. Štefánik, Liptovský Mikuláš, Slovak Republic

DOI:

https://doi.org/10.3849/aimt.01329

Keywords:

autocorrelation, covariance matrix, cross-correlation, near-field region, source localization

Abstract

The paper presents the mathematical model of the localization of electromagnetic sources in near-field region based on the sources localization mathematical model valid for the far-field region. The aim of the article is to show similarities and differences between both models with a deeper focus on near-field region model analysis using planar equidistant sensors array. Although both concepts in a high-level mathematical
description apparatus look very much the same, in details the near-field region model reconstruction process is more complex with different constrains. Detailed covariance matrix analysis and statistics of the covariance matrix represents the main part of the article. In the conclusion, paper shows some model verification results for the localization of single source, correlated sources and coherent sources.

Author Biographies

  • František Nebus, Armed Forces Academy of General M. R. Štefánik, Liptovský Mikuláš, Slovak Republic

    Department of Electronics

  • Stanislava Gažovová, Armed Forces Academy of General M. R. Štefánik, Liptovský Mikuláš, Slovak Republic

    Department of Electronics

References

GAŽOVOVÁ, S. and NEBUS, F. The Covariance Matrix Analysis for Near Field Sources Localization Model. In Proceedings of New Trends in Signal Processing. Demanovska Dolina: IEEE, 2018, p. 122-126. https://doi.org/10.23919/NTSP.2018.8524098.

PILLAI, S.U. Array Signal Processing. New York: Springer, 1989. 221 p. ISBN 978-1-4612-3632-0.

PISARENKO, F.V. The Retrieval of Harmonics from Covariance Functions. Geophysics Journal of the Royal Society, 1973, vol. 33, no. 3, p. 347-366. https://doi.org/10.1111/j.1365-246X.1973.tb03424.x.

GORBUNOVA, A.A. Stochastic Sources Localization Algorithm based on Near-Field Two-Point Planar Scanning Data (in Russian). Trudy MAI, 2014, no. 73. [viewed 2019-03-05]. Available from: http://trudymai.ru/eng/published.php?ID=48571

MUDROŇ, J. Fundamentals of Classical Electrodynamics (in Slovak). Liptovský Mikuláš: Armed Forces Academy of General M. R. Štefánik, 2013. 263 p. ISBN 978-80-8040-480-2.

NEBUS, F., KURTY, J. and MARKO, J. Controlled Spatial Smoothing for Coherent Signal Processing. In Proceedings of the Conference IST-039/RSY-011. Chester: ATO-RTO, 2003.

CHEN, Z., GOKEDA, G. and YU, Y. Introduction to Direction-of-Arrival Estimation. Northwood, MA: Artech House, 2010. 193 p. ISBN 978-1-59693-089-6.

WANG, H. and LIU, K.J.R. 2D Spatial Smoothing for Multipath Coherent Signal Separation Source. IEEE Transactions on Aerospace and Electronic Systems, 1998, vol. 34, no. 2, p. 391-405. https://doi.org/10.1109/7.670322.

Downloads

Published

13-03-2020

Issue

Section

Research Paper

Categories

How to Cite

The Covariance Matrix Analysis and Statistics for Near-field Sources Localization Model. (2020). Advances in Military Technology, 15(1), 149-162. https://doi.org/10.3849/aimt.01329

Similar Articles

1-10 of 57

You may also start an advanced similarity search for this article.