A Recovery Performance Study of Compressive Sensing Methods on Antenna Array Diagnosis from Near-Field Measurements
Keywords:
Antenna array diagnosis, antenna testing, compressive sensing, near-field, low SNRAbstract
Antenna testing consists locating the potential defaults from radiated field measurements. It has been established in literature, that compressive sensing methods provide faster results in failure detection from smaller number of measurement data compared to the traditional back-propagation mechanisms. Compressive sensing (CS) methods require a priori measurement of failure-free reference array and require small number of measurements for diagnosis. However, there are conflicting reports in literature regarding the choice of appropriate CS method, and there is no sufficient comparison study to justify which one is a better choice under a very harsh condition. In this study, recovery performance test of CS methods for the diagnosis of antenna array from few near-field measured data under various signal-to-noise ratios (SNRs) is presented. Specifically, we tested three prominent regularization procedures: total variation (TV), mixed l1/ l2 norm, and minimization of the l1 in solving diagnosis problems in antenna array. Linear system that relates the difference between near-field measured data from reference antenna (RA) array and array under test (AUT), and the difference that exist between coefficients of RA and the AUT, is solved by the three compressive sensing regularization methods. Numerical experiment of a 10 × 10 rectangular waveguide array under realistic noise scenario, operating at 10 GHz is used to conduct the test. Minimization l1 technique is more robust to additive data noise. It exhibits better diagnosis at 20 dB and 10 dB SNR, making it a better candidate for noisy measured data as compared to other techniques.
Downloads
References
J. A. Rodriguez, F. Ares, E. Moreno, and G. Franceschetti, “Genetic algorithm procedure for linear array failure correction,” Electron. Lett., vol. 36, no. 3, pp. 196-198, Feb. 2000.
R. Iglesias, F. Ares, M. Fernandez-Delgado, J. A. Rodriguez, J. Bregains, and S. Barro, “Element failure detection in linear antenna arrays using case-based reasoning,” IEEE Antennas Propag. Mag., vol. 50, no. 4, pp. 198-204, Aug. 2008.
O. M. Bucci, M. D. Migliore, G. Panariello, and P. Sgambato, “Accurate diagnosis of conformal arrays from near-field data using the matrix method,” IEEE Trans. Antennas Propag., vol. 53, no. 3, pp. 1114-1120, Mar. 2005.
J. A. Rodriguez-Gonzalez, F. Ares-Pena, M. Fernandez-Delgado, R. Iglesias, and S. Barro, “Rapid method for finding faulty elements in antenna arrays using far field pattern samples,” IEEE Trans. Antennas Propag., vol. 57, no. 6, pp. 1679-1683, June 2009.
A. Buonanno, and M. D’Urso, “On the diagnosis of arbitrary geometry fully active arrays,” in Proc. Eur. Conf. Antennas Propag. (EuCAP), Barcelona, Spain, pp. 1-4.S, Apr. 2010.
S. Clauzier, S. M. Mikki, and Y. M. M. Antar, “Design of near-field synthesis arrays through global optimization,” IEEE Trans. Antennas Propag., vol. 63, no. 1, pp. 151-165, Jan. 2015.
M. D. Migliore, “A compressed sensing approach for array diagnosis from a small set of near-field measurements,” IEEE Trans. Antennas Propag., vol. 59, no. 6, pp. 2127-2133, June 2011.
G. Oliveri, P. Rocca, and A. Massa, “Reliable diagnosis of large linear arrays—A Bayesian compressive sensing approach,” IEEE Trans. Antennas Propag., vol. 60, no. 10, pp. 4627-4636, Oct. 2012.
O. Famoriji, Z. Zhang, A. Fadamiro, R. Zakariyya, and F. Lin, “Planar array diagnostic tool for millimeter-wave wireless communication systems,” Electronics, vol. 7, no. 383, 2018.
O. J. Famoriji, Z. Zhang, A. Fadamiro, Z. Khan, and F. Lin, “Active antenna array diagnosis from far-field measurements,” IEEE Int. Conference on Integrated Circuits, Technologies and Applications (ICTA), Beijing, China, pp. 1-2.S, Dec. 2018.
B. Fuchs, L. L. Coq, and M. D. Migliore, “Fast antenna array diagnosis from a small number of far-field measurements,” IEEE Trans. Antennas Propag., vol. 64, no. 6, pp. 2227-2235, June 2016.
A. F. Morabito, R. Palmeri, and T. Isernia, “A compressive-sensing-inspired procedure for array antenna diagnostic by a small number of phaseless measurements,” IEEE Trans. Antennas Propag., vol. 64, no. 7, pp. 3260-3265, July 2016.
5G: A technological Vision, HUAWEI white paper, Huawei Technologies Co., Shenzhen [Online] Available: http://www.huawei. com/5gwhitepaper/
J. J. Lee, E. M. Ferrer, D. P. Woollen, and K. M. Lee, “Near-field probe used as a diagnostic tool to locate defective elements in an array antenna,” IEEE Trans. Antennas Propag., vol. 36, no. 3, pp. 884-889, June 1988.
L. Gattoufi, D. Picard, Y. R. Samii, and J. C. Bolomey, “Matrix method for near-field diagnostic techniques of phased array antennas,” in Proc. IEEE Int. Symp. Phased Array Syst. Technol., pp. 52-57, 1996.
C. Xiong, G. Xiao, Y. Hou, and M. Hameed, “A compressive sensing-based element failure diagnosis method for phased array antenna during beam steering,” IEEE Trans. Antennas Wireless Propag. Lett., vol. 18, pp. 1756-1760, Sept. 2019.
M. D. Migliore, “A compressed sensing approach for array diagnosis from a small set of near-field measurements,” IEEE Trans. Antennas Propag., vol. 59, no. 6, pp. 2127-2133, June 2011.
G. Oliveri, P. Rocca, and A. Massa, “Reliable diagnosis of large linear arrays, a Bayesian compressive sensing approach,” IEEE Trans. Antennas Propag., vol. 60 no. 10, pp. 4627-4636, Oct. 2012.
M. D. Migliore, “Array diagnosis from far-field data using the theory of random partial Fourier matrices,” IEEE Trans. Antennas Wireless Propag. Lett., vol. 12, pp. 745-748, July 2013.
B. Fuchs and M. D. Migliore, “Accurate array diagnosis from near-field measurements using reweighted minimization,” IEEE Antennas Propag. Symp., Orlando, FL, USA, pp. 2255-2256, 2013.
B. Fuchs, L. Le Coq, L. Ferro-Famil, and M. D. Migliore, “Comparison of methods for reflectarray diagnostic from far field measurements,” in Proc. IEEE Int. Symp. Antennas Propag., pp. 398-399, July 2015.
D. L. Donoho, “Compressive sensing,” IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 1289-1306, Apr. 2006.
E. J. Candes, J. Romberg, and T. Tao “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory, vol. 52, no. 2, pp. 489- 509, Feb. 2006.
D. L. Donoho and J. Tanner, “Precise undersampling theorems,” Proc. IEEE, vol. 98, no. 6, pp. 913-923, June 2010.
M. D. Migliore, B. Fuchs, L. Le Coq, and L. Ferro-Famil, “Compressed sensing approach for reflectarray diagnostic from far field measurements,” in Proc. Eur. Microw. Conf., Sep. pp. 289-292, 2015.
A. Massa, P. Rocca, and G. Oliveri, “Compressive sensing in electromagnetics—A review,” IEEE Antennas Propag. Mag., vol. 57, no. 1, pp. 224- 238, Feb. 2015.
