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An acceleration method employing sparse sensing matrix for fast analysis of the wide-angle electromagnetic problems based on compressive sensing

Published online by Cambridge University Press:  18 March 2024

Qi Qi Open the ORCID record for Qi Qi [Opens in a new window] ,
Xinyuan Cao ,
Yi Liu ,
Meng Kong ,
Xiaojing Kuang  and
Mingsheng Chen
Qi Qi
Affiliation:
Anhui Province Key Laboratory of Simulation and Design for Electronic Information System, Hefei Normal University, Hefei, China
Xinyuan Cao*
Affiliation:
Anhui Province Key Laboratory of Simulation and Design for Electronic Information System, Hefei Normal University, Hefei, China
Yi Liu
Affiliation:
Anhui Province Key Laboratory of Simulation and Design for Electronic Information System, Hefei Normal University, Hefei, China School of Computer Science and Technology, Hefei Normal University, Hefei, China
Meng Kong
Affiliation:
Anhui Province Key Laboratory of Simulation and Design for Electronic Information System, Hefei Normal University, Hefei, China
Xiaojing Kuang
Affiliation:
Anhui Province Key Laboratory of Simulation and Design for Electronic Information System, Hefei Normal University, Hefei, China
Mingsheng Chen
Affiliation:
Anhui Province Key Laboratory of Simulation and Design for Electronic Information System, Hefei Normal University, Hefei, China
*
Corresponding author: Xinyuan Cao; Email: xycaoBL@163.com
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Abstract

The electromagnetic scattering problem over a wide incident angle can be rapidly solved by introducing the compressive sensing theory into the method of moments, whose main computational complexity is comprised of two parts: a few calculations of matrix equations and the recovery of original induced currents. To further improve the method, a novel construction scheme of measurement matrix is proposed in this paper. With the help of the measurement matrix, one can obtain a sparse sensing matrix, and consequently the computational cost for recovery can be reduced by at least half. The scheme is described in detail, and the analysis of computational complexity and numerical experiments are provided to demonstrate the effectiveness.

Keywords

compressive sensingmeasurement matrixmethod of momentssparse sensing matrix
Type
Research Paper
Information
International Journal of Microwave and Wireless Technologies , First View , pp. 1 - 6
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Copyright
© The Author(s), 2024. Published by Cambridge University Press in association with The European Microwave Association.

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References

See, KY, Chua, EK and Liu, Z (2009) Accurate and efficient evaluation of MoM matrix based on a generalized analytical approach. Progress in Electromagnetics Research 94, 367–382.CrossRefGoogle Scholar
Erdemli, YE, Gong, J, Reddy, CJ and Volakis, JL (1998) Fast RCS pattern fill using AWE technique. IEEE Transactions on Antennas and Propagation 46(11), 1752–1753.CrossRefGoogle Scholar
Miller, EK (1998) Model-based parameter estimation in electromagnetic III: Applications to EM integral equations. IEEE Antennas and Propagation Magazine 40(3), 49–66.CrossRefGoogle Scholar
Zhang, H, Zhao, X, Lin, Z and Sha, W (2016) Fast monostatic scattering analysis based on Bayesian compressive sensing. Applied Computational Electromagnetics Society Journal 31(11), 1279–1285.Google Scholar
Chen, MS, Liu, FL, Du, HM and Wu, XL (2011) Compressive sensing for fast analysis of wide-angle monostatic scattering problems. IEEE Antennas and Wireless Propagation Letters 10, 1243–1246.CrossRefGoogle Scholar
Chai, SR and Guo, LX (2016) Compressive sensing for monostatic scattering from 3-D NURBS geometries. IEEE Transactions on Antennas and Propagation 64(8), 3545–3553.CrossRefGoogle Scholar
Liu, Y, Qi, Q, Cao, XY, Chen, MS, Deng, GQ, Huang, ZX and Wu, XL (2021) Application of two-dimensional compressive sensing to wavelet method of moments for fast analysis of wide-angle electromagnetic scattering problems. International Journal of Antennas and Propagation 2021, 1–9.Google Scholar
Candes, EJ, Romberg, J and Tao, T (2006) Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory 52(2), 489–509.CrossRefGoogle Scholar
Chen, MS, Sha, W and Wu, XL (2009) Solution of arbitrarily dimensional matrix equation in computational electromagnetics by fast lifting wavelet-like transform. International Journal for Numerical Methods in Engineering 80(8), 1124–1142.CrossRefGoogle Scholar
Cao, XY, Chen, MS and Wu, XL (2012) Sparse transform matrices and their application in the calculation of electromagnetic scattering problems. Chinese Physics Letters 30(2), .Google Scholar
Yang, M and Hoog de, F (2015) Orthogonal matching pursuit with thresholding and its application in compressive sensing. IEEE Transactions on Signal Processing 63(20), 5479–5486.CrossRefGoogle Scholar
Zhang, B, Liu, Y and Wang, K (2014) Restricted isometry property analysis for sparse random matrices. Journal of Electronics & Information Technology 36(1), 169–174.Google Scholar
Gilbert, A and Indyk, P (2010) Sparse recovery using sparse matrices. Proceedings of the IEEE 98(6), 937–947.CrossRefGoogle Scholar
Berinde, R, Gilbert, AC, Indyk, P, Karloff, H and Strauss, MJ (2008) Combining geometry and combinatorics: A unified approach to sparse signal recovery. In 46th Annual Allerton Conference, Illinois, USA.CrossRefGoogle Scholar
Mamaghanian, H, Khaled, N, Atienza, D and Vandergheynst, P (2011) Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes. IEEE Transactions on Biomedical Engineering 58(9), 2456–2466.CrossRefGoogle ScholarPubMed