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Phased antenna array reconstructive diagnostics using compressed sensing approach


G. Yu. Kuznetsov – Post-graduate Student, Department of Radiophysics, Antennas and Microwave Technics, Moscow Aviation Institute (National Research University) E-mail: V. S. Temchenko – Dr.Sc. (Eng.), Professor, Department of Radiophysics, Antennas and Microwave Technics, Moscow Aviation Institute (National Research University) E-mail:

Conventional methods of phased antenna array diagnostics include measurement of full set of field data in near or far field zone. The solution of diagnostics problem consists of several parts: reconstruction of current amplitude and phase for every radiating element, defect element position search, defect characterization and defect correction. Number of measurements and overall time of mea-surements become significantly large, and the efficiency and accuracy of post-processing algorithms degrades if the antenna under test (AUT) comprises a large number of elements. If AUT is an active antenna array, the overall number of measurements and measurement time must be low. Measurement time is limited by two factors: first, service life of active elements is limited; second, characteristics of transmit-receive modules (TRM) change with temperature. Along with conventional methods, compressive sensing-based (CS) antenna array diagnostics methods are currently developed. These methods allow significant reduction of measurement number in near- or far-field zone, if the number of defect elements is low, what is usually true in practice. In this paper, a two-step approach to phased antenna array diagnostics reliability improvement is considered. First, a small number of measurements in near-field are carried out for AUT. The difference between these measurements and measurements of a non-defect antenna array is used in CS-based reconstruction to sort the radiating elements in three groups: operating, potentially defect and defect elements. Any group may contain potentially defect elements. Second, for diagnostics reliability improvement a number of channel-wise measurements is carried out using a 180° phase shift in po-tentially defect element. Based on the results of these measurements, amplitude and phase of potentially defect elements is recon-structed, and then elements are finally sorted in two groups: operating and defect. Results of a linear phased array diagnostics based on l1-minimization are presented. This approach allows the classification of defect element faults, needed for following array excitation correction, and can be used for a wide range of phased antenna arrays.


  1. Bubnov G.G., Nikulin S.M., Serjakov JU.N., Fursov S.A. Kommutacionnyjj metod izmerenija kharakteristik FAR. M.: Radio i svjaz. 1988.
  2. Bakhrakh L.D., Kremeneckijj S.D., Kurochkin A.P., Usin V.A., SHifrin JA.S. Metody izmerenijj parametrov izluchajushhikh sistem v blizhnejj zone. L.: Nauka. 1989.
  3. Bucci O.M., Migliore M.D., Panariello G. Accurate diagnosis of conformal arrays from near-field data using the matrix method // IEEE Trans. on Antennas and Propagation. 2005. V. 53. № 3. R. 1114–1120.
  4. Wang J.J.H. An examination of theory and practices of planar near-field measurement // IEEE Trans. on Antennas and Propagation. 1988. V. 36. № 6. R. 11–17.
  5. Migliore M.D. A compressed sensing approach for array diagnosis from a small set of near-field measurements // IEEE Trans. on Antennas and Propagation. 2011. V. 59. № 6. P. 2127–2133.
  6. Kuznecov G.JU., Temchenko V.S. Kombinirovannyjj metod diagnostiki antennykh reshetok na osnove ogranichennogo nabora izmerenijj polevykh kharakteristik v blizhnejj zone // Naukoemkie tekhnologii. 2015. № 8. S. 48–53.
  7. Granichin O.N., Pavlenko D.V. Randomizacija poluchenija dannykh i ℓ1-optimizacija (opoznanie so szhatiem) // Avtomatika i telemekhanika. 2010. Vyp. 11. S. 3–28.
  8. Rauhut H. Compressive sensing and structured random matrices // Radon Series Comp. Appl. Math XX. 2009. P. 1–94.
  9. Hansen T.B. Complex-point dipole formulation of probe-corrected cylindrical and spherical near-field scanning of electromagnetic fields // IEEE Trans. on Antennas and Propagation. 2009. V. 57. № 3. P. 728–741.
  10. Kim S.-J., Koh K., Lustig M., Boyd S. An interior-point method for large-scale l1-regularized least-squares // IEEE Journal of Selected Topics in Signal Processing. 2007. V. 1. № 4. P. 606–617.
  11. Figueredo M.A.T., Nowak R.D., Wright S.J. Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems // IEEE Journal of Selected Topics In Signal Processing. 2007. V. 1. № 4. P. 586–597.
  12. Zambrano M.N., Arias F.X., MedinaC.A. Comparative analysis of sparse signal reconstruction algorithms for compressed sensing // Excellence in Engineering To Enhance a Country’s Productivity. July 22–24, Ecuador.
  13. Yang J, Zhang Y. Alternating direction algorithms for l1-problems in compressive sensing // Society for Industrial and Applied Mathematics. 2011. V. 33. № 1. R. 250–278.


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May 29, 2020

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