×

Convolutional neural network-based perturbation shooting and bouncing rays solution for recognition of targets with uncertain geometrical shapes. (English) Zbl 1521.74355


MSC:

74S15 Boundary element methods applied to problems in solid mechanics
65N38 Boundary element methods for boundary value problems involving PDEs
68T07 Artificial neural networks and deep learning
78A46 Inverse problems (including inverse scattering) in optics and electromagnetic theory
Full Text: DOI

References:

[1] Catapano, I.; Affinito, A.; Moro, A. D.; Alli, G.; Soldovieri, F., Forward-looking ground-penetrating radar via a linear inverse scattering approach, IEEE Trans Geosci Remote Sens, 53, 10, 5624-5633 (2015)
[2] Rekanos, I. T., Neural-network-based inverse-scattering technique for online microwave medical imaging, IEEE Trans Magn, 38, 2, 1061-1064 (2002)
[3] Wang, W.; Jing, L.; Li, Z.; Murch, R. D., Utilizing the Born and Rytov inverse scattering approximations for detecting soft faults in lossless transmission lines, IEEE Trans Antennas Propag, 65, 12, 7233-7243 (2017)
[4] Cui, T. J.; Chew, W. C.; Yin, X. X.; Hong, W., Study of resolution and super resolution in electromagnetic imaging for half-space problems, IEEE Trans Antennas Propag, 52, 6, 1398-1411 (2004) · Zbl 1368.78070
[5] Hassan, A. M.; Bowman, T. C.; El-Shenawee, M., Efficient microwave imaging algorithm based on hybridization of the linear sampling and level set methods, IEEE Trans Antennas Propag, 61, 7, 3765-3773 (2013) · Zbl 1370.78175
[6] Rabbani, M.; Tavakoli, A.; Dehmollaian, M., A hybrid quantitative method for inverse scattering of multiple dielectric objects, IEEE Trans Antennas Propag, 64, 3, 977-987 (2016) · Zbl 1374.78038
[7] Cun, Y. L.; Bengio, Y.; Hinton, G., Deep learning, Nature, 521, 28, 436-444 (2015)
[8] Li, L.; Wang, L. G.; Teixeira, F. L.; Liu, C.; Nehorai, A.; Cui, T. J., DeepNIS: deep neural network for nonlinear electromagnetic inverse scattering, IEEE Trans Antennas Propag, 67, 3, 1819-1825 (2019)
[9] Wei, Z.; Chen, X., Deep-learning schemes for full-wave nonlinear inverse scattering, IEEE Trans Geosci Remote Sens, 57, 4, 1849-1860 (2019)
[10] Lee, S.; Park, S.; Kim, K., Improved classification performance using ISAR images and trace transform, IEEE Trans Aerosp Electron Syst, 53, 2, 950-965 (2017)
[11] A.C. Polycarpou, “Introduction to the Finite Element Method in Electromagnetics,” Introduction to the Finite Element Method in Electromagnetics, Morgan & Claypool, 2006.
[12] Ling, H.; Chou, R. C.; Lee, S. W., Shooting and bouncing rays-calculating the RCS of an arbitrarily shaped cavity, IEEE Trans Antennas Propag, 37, 2, 194-205 (1989)
[13] Baldauf, J.; Lee, S. W.; Lin, L.; Jeng, S. K.; Scarborough, S. M.; Yu, C. L., High frequency scattering from trihedral corner reflectors and other benchmark targets: SBR versus experiment, IEEE Trans Antennas Propag, 39, 9, 1345-1351 (1991)
[14] Suk, S. H.; Seo, T. I.; Park, H. S.; Kim, H. T., Multi-resolution grid algorithm in the SBR and its application to the RCS calculation, Microw Opt Technol Lett, 29, 6, 394-397 (2001)
[15] Xu, K.; Ding, D.; Chen, R., Programmable graphics processing units (GPUs) accelerated SBR method for analyzing the scattering of open cavities, (2008 Asia-Pacific Microwave Conference (2008)), 1-4
[16] Dong, C.; Guo, L.; Meng, X.; Wang, Y., An accelerated SBR for EM scattering from the electrically large complex objects, IEEE Antennas Wirel Propag Lett, 17, 12, 2294-2298 (2018)
[17] Fan, T.; Guo, L.; Lv, B.; Liu, W., An improved backward SBR-PO/PTD hybrid method for the backward scattering prediction of an electrically large target, IEEE Antennas Wirel Propag Lett, 15, 512-515 (2016)
[18] Cong, Z.; He, Z.; Chen, R., An efficient volumetric SBR method for electromagnetic scattering from In-Homogeneous plasma sheath, IEEE Access, 7, 90162-90170 (2019)
[19] Bhalla, R.; Ling, H., Three-dimensional scattering center extraction using the shooting and bouncing ray technique, IEEE Trans Antennas Propag, 44, 11, 1445-1453 (1996)
[20] Mahdaoui, A. E.; Ouahabi, A.; Moulay, M. S., Multilevel fast multipole acceleration for fast ISAR imaging based on compressive sensing, (2018 International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM) (2018)), 1-5
[21] Yun, D.; Lee, J.; Bae, K.; Lim, H.; Myung, N., Accurate and fast ISAR image formation for complex CAD using the shooting and bouncing ray, (2015 Asia-Pacific Microwave Conference (APMC) (2015)), 1-3
[22] Jamroz, B. F.; Williams, D. F.; Rezac, J. D.; Frey, M.; Koepke, A. A., Accurate Monte Carlo uncertainty analysis for multiple measurements of microwave systems, (2019 IEEE MTT-S International Microwave Symposium (IMS) (2019)), 1279-1282
[23] Fox, B., Strategies for quasi-monte carlo (1999), Kluwer: Kluwer Dordrecht, the Netherlands
[24] Minasny, B.; Vrugt, J. A.; McBratney, A. B., Confronting uncertainty in model-based geostatistics using Markov Chain Monte Carlo simulation, Geoderma, 163, 150-162 (2011)
[25] Masly, E., Statistical analysis of fourier transform estimates: monte Carlo and Stratified Sampling, (2006 IEEE International Symposium on Signal Processing and Information Technology (2006)), 739-744
[26] Tomy, G. J.K.; Vinoy, K. J., A fast polynomial chaos expansion for uncertainty quantification in stochastic electromagnetic problems, IEEE Antennas Wirel Propag Lett, 18, 10, 2120-2124 (2019)
[27] Zubac, Z.; De Zutter, D.; Vande Ginste, D., Scattering from two-dimensional objects of varying shape combining the multilevel fast multipole method (MLFMM) with the Stochastic Galerkin Method (SGM), IEEE Antennas Wirel Propag Lett, 13, 1275-1278 (2014)
[28] Zubac, Z.; Daniel, L.; Zutter, D. D.; Ginste, D. V., A Cholesky-Based SGM-MLFMM for stochastic full-wave problems described by correlated random variables, IEEE Antennas Wirel Propag Lett, 16, 776-779 (2017)
[29] Wang, K. C.; He, Z.; Ding, D. Z.; Chen, R. S., An effective stochastic method for uncertainty scattering analysis of 3-D objects with varying shape, (2018 International Applied Computational Electromagnetics Society Symposium - China (ACES) (2018)), 1-2
[30] Wang, K. C.; He, Z.; Ding, D. Z.; Chen, R. S., Uncertainty scattering analysis of 3-D objects with varying shape based on method of moments, IEEE Trans Antennas Propag, 67, 4, 2835-2840 (2019)
[31] He, Z.; Li, Y. S.; Zhao, Y.; Wan, J.; Yin, H. C.; Chen, R. S., Uncertainty RCS computation for multiple and multilayer thin medium-coated conductors by an improved TDS approximation, IEEE Trans Antennas Propag (2020), early access
[32] Zhang, Z.; Wang, H.; Xu, F.; Jin, Y., Complex-Valued convolutional neural network and its application in polarimetric SAR image classification, IEEE Trans Geosci Remote Sens, 55, 12, 7177-7188 (2017)
[33] Toumi, A.; Housseini, A. E.; Khenchaf, A., Aircrafts recognition using convolutional neurons network, (International Conference on Radar Systems (Radar 2017) (2017)), 1-4
[34] Bhalla, R.; Ling, H., Image domain ray tube integration formula for the shooting and bouncing ray technique, Radio Sci, 30, 5, 1435-1446 (1995)
[35] Yun, D. J.; Lee, J. I.; Yoo, J. H.; Myung, N. H., Fast bistatic ISAR image generation for realistic cad model using the shooting and bouncing ray technique, (2014 Asia-Pacific Microwave Conference (2014)), 1324-1326
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.