Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-25T08:14:25.491Z Has data issue: false hasContentIssue false
  • English
  • Français

Modifications of active phased antenna arrays near-field diagnosis method based on compressive sensing

Published online by Cambridge University Press:  18 July 2019

Grigory Kuznetsov Open the ORCID record for Grigory Kuznetsov [Opens in a new window] ,
Vladimir Temchenko ,
Maxim Miloserdov  and
Dmitry Voskresenskiy
Grigory Kuznetsov*
Affiliation:
Research Institute of Precision Instruments, Moscow, Russia
Vladimir Temchenko
Affiliation:
Department of Radioelectronics, Moscow Aviation Institute, Moscow, Russia
Maxim Miloserdov
Affiliation:
Research Institute of Precision Instruments, Moscow, Russia
Dmitry Voskresenskiy
Affiliation:
Department of Radioelectronics, Moscow Aviation Institute, Moscow, Russia
*
Author for correspondence: Grigory Kuznetsov, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper presents two modifications of compressive sensing (CS)-based approach applied to the near-field diagnosis of active phased arrays. CS-based antenna array diagnosis allows a significant reduction of measurement time, which is crucial for the characterization of electrically large active antenna arrays, e.g. used in synthetic aperture radar. However, practical implementation of this method is limited by two factors: first, it is sensitive to thermal instabilities of the array under test, and second, excitation reconstruction accuracy strongly depends on the accuracy of the elements of the measurement matrix. First proposed modification allows taking into account of thermal instability of the array by using an iterative ℓ1-minimization procedure. The second modification increases the accuracy of reconstruction using several simple additional measurements.

Keywords

Antenna arraydiagnosticscompressive sensinginverse problemthermal instability
Type
MIKON 2018
Information
International Journal of Microwave and Wireless Technologies , Volume 11 , Special Issue 7: MIKON 2018 / EuMW 2018 (Part II) , September 2019 , pp. 568 - 576
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Bachmann, M, Schwerdt, M and Brautigam, B (2009) Accurate antenna pattern modeling for phased array antennas in SAR applications – demonstration on TerraSAR-X. International Journal of Antennas and Propagation 2009, Article ID 492505.Google Scholar
2.Stangl, M, Werninghaus, R, Schweizer, B, Fischer, C, Brandfass, M, Mittermayer, J and Breit, H (2006) TerraSAR-X technologies and first results. IEE Proceedings – Radar, Sonar and Navigation 2, 86–95.Google Scholar
3.Brautigam, B, Schwerdt, M and Bachmann, M (2009) An efficient method for performance monitoring of active phased array antennas. IEEE Transactions on Geoscience and Remote Sensing 4, 1236–1243.Google Scholar
4.Sanchez, V, Martin, F, Barrio, A, Pinto, I, Garcia, R, Sierra, M, De Haro, L, Besada, JL and Galocha, B (2017) Measurement of BepiColombo mission medium gain antenna parameters under realistic thermal conditions. International Journal of Microwave and Wireless Technologies 9, 1409–1418.Google Scholar
5.Kuznetsov, GY, Miloserdov, MS, Temchenko, VS, Kovalenko, AI, Voskresenski, DI, Vnotchenko, SL, Riman, VV and Shishanov, AV (2017) Practical aspects of active phased arrays characterization during thermal testing. Progress in Electromagnetics Research Symposium – Spring (PIERS), St. Petersburg, Russia.Google Scholar
6.Bucci, OM, Migliore, MD and Panariello, G (2005) Accurate diagnosis of conformal arrays from near-field data using the matrix method. IEEE Transactions on Antennas and Propagation 3, 1114–1120.Google Scholar
7.Wang, JJH (1988) An examination of theory and practices of planar near-field measurement. IEEE Transactions on Antennas and Propagation 6, 746–753.Google Scholar
8.Alvarez, Y, Las-Heras, F and Garcia, C (2012) The sources reconstruction method for antenna diagnostics and imaging applications. Solutions and Applications of Scattering, Propagation, Radiation and Emission of Electromagnetic Waves, IntechOpen, London, pp. 159–186.Google Scholar
9.Migliore, MD (2011) A compressed sensing approach for array diagnosis from a small set of near-field measurements. IEEE Transactions on Antennas and Propagation 6, 2127–2133.Google Scholar
10.Rauhut, H (2009) Compressive sensing and structured random matrices. Radon Series on Computational and Applied Mathematics 9, 1–94.Google Scholar
11.Shah, P and Khankhoje, UK and Moghaddam, M (2016) Inverse scattering using a joint L1-L2 norm-based regularization. IEEE Transactions on Antennas and Propagation 4, 1373–1384.Google Scholar
12.Turk, AS, Ozkan-Bakbak, P, Durak-Ata, L, Orhan, M and Unal, M (2016) High-resolution signal processing techniques for through-the-wall imaging radar systems. International Journal of Microwave and Wireless Technologies 8, 855–863.Google Scholar
13.Li, W, Deng, W and Migliore, MD (2018) A deterministic far-field sampling strategy for array diagnosis using sparse recovery. IEEE Antennas and Wireless Propagation Letters 7, 1261–1265.Google Scholar
14.Pinchera, D and Migliore, MD (2017) Failure identification and pattern correction in large isophoric sparse arrays. European Conference on Antennas and Propagation (EUCAP), Paris, France.Google Scholar
15.Kuznetsov, GY, Miloserdov, MS, Bulygin, ML, Temchenko, VS, Kovalenko, AI and Riman, VV (2018) Near-field measurement techniques for space-borne SAR digital phased array antennas. 12 European Conference on Synthetic Aperture Radar (EUSAR 2018), Aachen, Germany.Google Scholar
16.Kuznetsov, GY, Temchenko, VS, Miloserdov, MS and Voskresenskiy, DI (2018) Phased antenna array reconstructive diagnostics using small number of measurements. 2018 Baltic URSI Symposium, Poznan, Poland.Google Scholar
17.Hansen, TB (2009) Complex-point dipole formulation of probe-corrected cylindrical and spherical near-field scanning of electromagnetic fields. IEEE Transactions on Antennas and Propagation 3, 728–741.Google Scholar
18.Kuznetsov, Y, Baev, A, Gorbunova, A, Konovalyuk, M, Thomas, D, Smartt, C, Baharuddin, MH, Russer, JA and Russer, P (2016) Localization of the equivalent sources on the PCB surface by using ultra-wideband time domain near-field measurements. 2016 International Symposium on Electromagnetic Compatibility – EMC Europe, Wroclaw, Poland.Google Scholar
19.Yang, J and Zhang, Y (2011) Alternating direction algorithms for l 1-problems in compressive sensing. Society for Industrial and Applied Mathematics 1, 250–278.Google Scholar
20.Becker, S, Bobin, J and Candes, EJ (2011) NESTA: a fast and accurate first-order method for sparse recovery. SIAM Journal on Imaging Sciences 4, 1–39.Google Scholar
21.Nesterov, Y (2005) Smooth minimization of non-smooth functions. Mathematical Programming 5, 127–152.Google Scholar
22.Selesnick, I (2017) Sparse regularization via convex analysis. IEEE Transactions on Signal Processing 9, 4481–4494.Google Scholar
23.Wei, Z, Zhang, B, Han, B, Xu, Z, Hong, W and Wu, Y (2018) An accurate SAR imaging method based on generalized minimax concave penalty. 12 European Conference on Synthetic Aperture Radar (EUSAR 2018), Aachen, Germany.Google Scholar
24.IEEE recommended practice for near-field antenna measurements (2012). https://doi.org/10.1109/IEEESTD.2012.6375745Google Scholar