Numerical Assessment of Reflectarray Applicability to CS-based DoA Estimation

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dc.AffiliationOctober University for modern sciences and Arts (MSA)
dc.contributor.authorSkoblikov, Sergii
dc.contributor.authorIbrahim, Mohamed
dc.contributor.authorRömer, Florian
dc.contributor.authorS. Thomä, Reiner
dc.date.accessioned2020-03-07T08:26:23Z
dc.date.available2020-03-07T08:26:23Z
dc.date.issued2015
dc.descriptionMSA Google Scholaren_US
dc.description.abstractThis paper examines the performance of tunable reflectarray for Direction of Arrival (DoA) estimation based on Compressed Sensing (CS). Using a reflectarray lifts the limitation on array size compared to classical antenna arrays and provides significantly increased number and complexity of the Sensing Functions (SFs), which boosts the performance of the CS. The paper presents the data model of the reflectarray and analyzes its performance in terms of manifold spatial correlation.en_US
dc.description.sponsorshipIEEEen_US
dc.identifier.citation[1] M. A. Davenport, M. F. Duarte, Y. C. Eldar, and G. Kutyniok, Compressed Sensing: Theory and Applications. Cambridge University Press, 2012, ch. 1, pp. 1 – 64. [2] M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Processing Magazine, p. 83, March 2008. [3] M. Ibrahim, F. R¨omer, R. Alieiev, G. Del Galdo, and R. S. Thom¨a, “On the estimation of grid offsets in CS-based direction-of-arrival estimation,” in Proc. IEEE Int. Conf. Acoustics, Speech and Sig. Proc. (ICASSP 2014), Florence, Italy, May 2014. [4] L. Carin, “On the relationship between compressive sensing and random sensor arrays,” IEEE Antennas and Propagation Magazine, vol. 51, no. 5, pp. 72–82, October 2009. [5] Y. Wang, G. Leus, and A. Pandharipande, “Direction estimation using compressive sampling array processing,” in IEEE/SP 15th Workshop on Statistical Signal Processing, 2009, pp. 626 – 629. [6] D. Berry, R. Malech, and W. Kennedy, “The reflectarray antenna,” IEEE Transactions on Antennas and Propagation, vol. 11, no. 6, pp. 645–651, 1963. [7] C.-C. Cheng and A. Abbaspour-Tamijani, “Evaluation of a novel topology for MEMS programmable reflectarray antennas,” Microwave Theory and Techniques, IEEE Transactions on, vol. 57, no. 12, pp. 3333–3344, 2009. [8] D. Mackenzie, “Compressed sensing makes every pixel count,” What’s Happening in the Math. Sciences, pp. 114 – 127, 2009, AMS. [9] H. Rajagopalan, S. Xu, and Y. Rahmat-Samii, “On understanding the radiation mechanism of reflectarray antennas: An insightful and illustrative approach,” Antennas and Propagation Magazine, IEEE, vol. 54, no. 5, pp. 14–38, 2012. [10] P.-S. Kildal, Foundations of Antenna Engineering: A Unified Approach for Line-Of-Sight and Multipath. Artech House Publishers, May 2015. [11] M. Ibrahim, F. R¨omer, and G. Del Galdo, “On the design of the measurement matrix for compressed sensing based DoA estimation,” in International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brisbane, Australia, April 2015.en_US
dc.identifier.urihttps://t.ly/xqvVK
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2015 16th International Radar Symposium (IRS);Pages : 404-409
dc.subjectUniversity of Numerical assessment; DoA estimationen_US
dc.titleNumerical Assessment of Reflectarray Applicability to CS-based DoA Estimationen_US
dc.typeBook chapteren_US

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