Vol. 107
Latest Volume
All Volumes
PIERC 142 [2024] PIERC 141 [2024] PIERC 140 [2024] PIERC 139 [2024] PIERC 138 [2023] PIERC 137 [2023] PIERC 136 [2023] PIERC 135 [2023] PIERC 134 [2023] PIERC 133 [2023] PIERC 132 [2023] PIERC 131 [2023] PIERC 130 [2023] PIERC 129 [2023] PIERC 128 [2023] PIERC 127 [2022] PIERC 126 [2022] PIERC 125 [2022] PIERC 124 [2022] PIERC 123 [2022] PIERC 122 [2022] PIERC 121 [2022] PIERC 120 [2022] PIERC 119 [2022] PIERC 118 [2022] PIERC 117 [2021] PIERC 116 [2021] PIERC 115 [2021] PIERC 114 [2021] PIERC 113 [2021] PIERC 112 [2021] PIERC 111 [2021] PIERC 110 [2021] PIERC 109 [2021] PIERC 108 [2021] PIERC 107 [2021] PIERC 106 [2020] PIERC 105 [2020] PIERC 104 [2020] PIERC 103 [2020] PIERC 102 [2020] PIERC 101 [2020] PIERC 100 [2020] PIERC 99 [2020] PIERC 98 [2020] PIERC 97 [2019] PIERC 96 [2019] PIERC 95 [2019] PIERC 94 [2019] PIERC 93 [2019] PIERC 92 [2019] PIERC 91 [2019] PIERC 90 [2019] PIERC 89 [2019] PIERC 88 [2018] PIERC 87 [2018] PIERC 86 [2018] PIERC 85 [2018] PIERC 84 [2018] PIERC 83 [2018] PIERC 82 [2018] PIERC 81 [2018] PIERC 80 [2018] PIERC 79 [2017] PIERC 78 [2017] PIERC 77 [2017] PIERC 76 [2017] PIERC 75 [2017] PIERC 74 [2017] PIERC 73 [2017] PIERC 72 [2017] PIERC 71 [2017] PIERC 70 [2016] PIERC 69 [2016] PIERC 68 [2016] PIERC 67 [2016] PIERC 66 [2016] PIERC 65 [2016] PIERC 64 [2016] PIERC 63 [2016] PIERC 62 [2016] PIERC 61 [2016] PIERC 60 [2015] PIERC 59 [2015] PIERC 58 [2015] PIERC 57 [2015] PIERC 56 [2015] PIERC 55 [2014] PIERC 54 [2014] PIERC 53 [2014] PIERC 52 [2014] PIERC 51 [2014] PIERC 50 [2014] PIERC 49 [2014] PIERC 48 [2014] PIERC 47 [2014] PIERC 46 [2014] PIERC 45 [2013] PIERC 44 [2013] PIERC 43 [2013] PIERC 42 [2013] PIERC 41 [2013] PIERC 40 [2013] PIERC 39 [2013] PIERC 38 [2013] PIERC 37 [2013] PIERC 36 [2013] PIERC 35 [2013] PIERC 34 [2013] PIERC 33 [2012] PIERC 32 [2012] PIERC 31 [2012] PIERC 30 [2012] PIERC 29 [2012] PIERC 28 [2012] PIERC 27 [2012] PIERC 26 [2012] PIERC 25 [2012] PIERC 24 [2011] PIERC 23 [2011] PIERC 22 [2011] PIERC 21 [2011] PIERC 20 [2011] PIERC 19 [2011] PIERC 18 [2011] PIERC 17 [2010] PIERC 16 [2010] PIERC 15 [2010] PIERC 14 [2010] PIERC 13 [2010] PIERC 12 [2010] PIERC 11 [2009] PIERC 10 [2009] PIERC 9 [2009] PIERC 8 [2009] PIERC 7 [2009] PIERC 6 [2009] PIERC 5 [2008] PIERC 4 [2008] PIERC 3 [2008] PIERC 2 [2008] PIERC 1 [2008]
2020-11-30
Improved Enumeration of Scatterers Using Multifrequency Data Fusion in MDL for Microwave Imaging Applications
By
Progress In Electromagnetics Research C, Vol. 107, 65-79, 2021
Abstract
This paper presents a modified version of minimum description length (MDL) method, referred as multifrequency MDL (FMDL), for scatterers enumeration before using the multiple signal classification (MUSIC) algorithm in microwave imaging applications. The inclusion of data from multiple frequencies should make an attempt to reduce the error in number estimation due to noise and multiple scattering. Data fusion in multiple frequencies is performed based on two schemes called averaging and maximization rules. The solution for MDL criterion which is a minimum for one frequency is not likely to be the solution for other frequencies, so by averaging the MDL criterion over the total frequencies or by maximization of the solution for each frequency, we can achieve the correct source number. Furthermore, a whitening step before applying FMDL method is employed to compensate the aspect limitations of the measured data due to the limited number of antennas. The superiority of the proposed FMDL approach with respect to the other competing methods is confirmed by both the numerical examples and the Institut Fresnel experimental dataset. The results indicate that the FMDL yields more accurate estimate of the targets number specially for the cases of low SNR values and very colsely spaced scatterers.
Citation
Roohallah Fazli, Hadi Owlia, and Majid Pourahmadi, "Improved Enumeration of Scatterers Using Multifrequency Data Fusion in MDL for Microwave Imaging Applications," Progress In Electromagnetics Research C, Vol. 107, 65-79, 2021.
doi:10.2528/PIERC20091803
References

1. Benny, R., T. A. Anjit, and P. Mythili, "An overview of microwave imaging for breast tumor detection," Progress In Electromagnetics Research B, Vol. 87, 61-91, 2020.
doi:10.2528/PIERB20012402

2. Park, W.-K., K.-J. Lee, H.-P. Kim, and S.-H. Son, "Application of MUSIC to microwave imaging for detection of dielectric anomalies," Progress In Electromagnetics Research Symposium — Spring (PIERS), 2908-2912, St. Petersburg, Russia, May 22–25, 2017.

3. Lee, K. J., S. H. Son, and W. K. Park, "A real-time microwave imaging of unknown anomaly with and without diagonal elements of scattering matrix," Results in Physics, Vol. 17, 103104, 2020.
doi:10.1016/j.rinp.2020.103104

4. Agarwal, K. and X. Chen, "Applicability of MUSIC-type imaging in two-dimensional electromagnetic inverse problems," IEEE Trans. Antennas Propag., Vol. 56, No. 10, 3217-3223, 2008.
doi:10.1109/TAP.2008.929434

5. Ammari, H., E. Lakovleva, and D. Lesselier, "A MUSIC algorithm for locating small inclusions in a half-space from scattering amplitude at a fixed frequency," SIAM Multiscale Model. Simul., Vol. 3, 597-628, 2005.
doi:10.1137/040610854

6. Fazli, R., M. Nakhkash, and A. A. Heidari, "Alleviating the practical restrictions for MUSIC algorithm in actual microwave imaging systems: Experimental assessment," IEEE Trans. Antennas Propag., Vol. 62, No. 6, 3108-3118, 2014.
doi:10.1109/TAP.2014.2313632

7. Solimene, R., A. DellAversano, and G. Leone, "Interferometric time reversal MUSIC for small scatterer localization," Progress In Electromagnetics Research, Vol. 131, 243-258, 2012.
doi:10.2528/PIER12062103

8. Wax, W. and T. Kailath, "Detection of signals by information theoretic criteria," IEEE Trans. on Acoustic, Speech, and Signal Processing, Vol. 33, 387-392, 1985.
doi:10.1109/TASSP.1985.1164557

9. Xiao, M., P. Wei, and H. M. Tai, "Estimation of the number of sources based on hypothesis testing," Journal of Communications and Networks, Vol. 14, No. 5, 481-486, 2012.
doi:10.1109/JCN.2012.00004

10. He, Z., A. Cichocki, S. Xie, and K. Choi, "Detection the number of clusters in n-way probabilistic clustering," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 32, No. 11, 2006-2021, 2010.
doi:10.1109/TPAMI.2010.15

11. Liavas, A. P. and P. A. Regalia, "On the behavior of information theoretic criteria for model order selection," IEEE Trans. on Signal Processing, Vol. 49, No. 8, 1689-1695, 2001.
doi:10.1109/78.934138

12. Pouramadi, M., M. Nakhkash, and A. A. Tadion, "Application of MDL criterion for microwave imaging by MUSIC algorithm," Progress In Electromagnetics Research B, Vol. 40, 261-278, 2012.
doi:10.2528/PIERB12031001

13. Fazli, R. and M. Nakhkash, "An analytical approach to estimate the number of small scatterers in 2D inverse scattering problems," Inverse Prob., Vol. 28, No. 7, 75012-75033, 2012.
doi:10.1088/0266-5611/28/7/075012

14. Wax, M. and I. Ziskind, "Detection of the number of coherent signals by the MDL principle," IEEE Trans. on Acoustic, Speech, and Signal Processing, Vol. 37, No. 8, 1190-1196, 1989.
doi:10.1109/29.31267

15. Wax, W. and T. Kailath, "Detection of signals by information theoretic criteria," IEEE Trans. on Acoustic, Speech, and Signal Processing, Vol. 33, 387-392, 1985.
doi:10.1109/TASSP.1985.1164557

16. Ding, Q. and S. Kay, "Inconsistency of the MDL: On the performance of model order selection criteria with increasing signal-to-noise ratio," IEEE Trans. on Signal Processing, Vol. 59, 1959-1969, 2011.
doi:10.1109/TSP.2011.2108293

17. Ridder, F., R. Pintelon, J. Schoukens, and D. P. Gillikin, "Modified AIC and MDL model selection criteria for short data records," IEEE Trans. on Instrumentation and Measurement, Vol. 54, 144-150, 2005.
doi:10.1109/TIM.2004.838132

18. Kundu, D., "Estimating the number of signals in the presence of white noise," Journal of Statistical Planning and Inference Elsevier, Vol. 90, No. 4, 57-68, 2000.
doi:10.1016/S0378-3758(00)00102-6

19. Belkebir, K. and M. Saillard, "Testing inversion algorithms against experimental data," Inverse Probl., Vol. 17, 1565-1571, 2001.
doi:10.1088/0266-5611/17/6/301

20. Gilmore, C., P. Mojabi, A. Zakaria, M. Ostadrahimi, C. Kaye, S. Noghanian, L. Shafai, S. Pistorius, and J. Lovetri, "A wideband microwave tomography system with a novel frequency selection procedure," IEEE Trans. Biomedical Eng., Vol. 57, No. 4, 894-904, 2010.
doi:10.1109/TBME.2009.2036372

21. Rocca, P., M. Donelli, G. L. Gragnani, and A. Massa, "Iterative multi-resolution retrieval of non-measurable equivalent currents for the imaging of dielectric objects," Inverse Probl., Vol. 25, 055004-055018, 2009.
doi:10.1088/0266-5611/25/5/055004

22. Caorsi, S., M. Donelli, A. Lommi, and A. Massa, "Location and imaging of two-dimensional scatterers by using a particle swarm algorithm," Journal of Electromagnetic Waves and Applications, Vol. 18, No. 4, 481-494, 2004.
doi:10.1163/156939304774113089

23. Zheng, H., M. Z. Wang, Z. Q. Zhao, and L. L. Li, "A novel linear EM reconstruction algorithm with phaseless data," Progress In Electromagnetics Research Letters, Vol. 14, 133-146, 2010.
doi:10.2528/PIERL10031306

24. Caorsi, S., M. Donelli, and A. Massa, "Detection, location, and imaging of multiple scatterers by means of the iterative multiscaling method," IEEE Trans. on Microwave Theory and Tech., Vol. 52, 1217-1228, 2004.
doi:10.1109/TMTT.2004.825699