volume | PIER Journals

Abstract

Microwave imaging radar systems are often required for the recognition of hidden objects at various job sites. Most existing imaging methods that these systems employ, such as beamforming, diffraction tomography, and compressed sensing, which operate on synthetic aperture radar, produce highly distorted radar images due to the limitation of the frequency range, size of the array, and attenuation during the propagation, and thereby become hard to interpret the description of the object. Several methods explored for the recognition of hidden objects are based on deep neural network models with millions of parameters and high computational costs that render them unusable in portable devices. Moreover, most methods have been evaluated on datasets of microwave radar images of hidden objects with the same relative permittivity, orientation, size, and position. In real-time scenarios, objects may not have similar relative permittivity, orientation, size, and position. Due to variation in the object's relative permittivity, orientation, size, and position, there will also be variation in the reflectivity. Consequently, it is hard to say if those algorithms will be robust in real-world conditions. This paper presents a novel shape-based approach for recognizing hidden objects which combines delay-and-sum beamforming with an artificial neural network. The merit of this proposed method is its ability to simultaneously recognize and reconstruct the object's actual shape from distorted microwave radar images irrespective of any variation in relative permittivity, orientation, size, and position of hidden object. The performance of the developed technique was tested on a dataset of microwave radar images of various hidden objects having different relative permittivities, sizes, orientations, and positions. The proposed method yielded an average reconstruction rate of 91.6%. The proposed method is appropriate for evaluating occluded objects such as utility infrastructure, assets, and weapons detection and interpretation, which have regular shapes and sizes of the cross-section at various construction, archaeological and forensic sites.

1. Zubair Akhter, A. B. N. and M. J. Akhtar, "Hemisphere lens-loaded Vivaldi antenna for time domain microwave imaging of concealed objects," Journal of Electromagnetic Waves and Applications, Vol. 30, 1183-1197, 2016.
doi:10.1080/09205071.2016.1186574

2. Tan, W., P. Huang, Z. Huang, Y. Qi, and W. Wang, "Three-dimensional microwave imaging for concealed weapon detection using range stacking technique," International Journal of Antennas and Propagation, 2017.

3. Zheng, Z., J. Pan, Z. Ni, C. Shi, S. Ye, and G. Fang, "Human posture reconstruction for through-the-wall radar imaging using convolutional neural networks," IEEE Geoscience and Remote Sensing Letters, 1-5, 2021.

4. Lombardi, F., M. Lualdi, F. Picetti, P. Bestagini, G. Janszen, and L. A. Di Landro, "Ballistic ground penetrating radar equipment for blast-exposed security applications," Remote Sensing, Vol. 12, 717, 2020.
doi:10.3390/rs12040717

5. Chen, B., T. Jin, B. Lu, and Z. Zhou, "Building interior layout reconstruction from through-the-wall radar image using MST-based method," EURASIP Journal on Advances in Signal Processing, Vol. 31, 1-9, 2014.

6. Singh, A. P., S. Dwivedi, and P. K. Jain, "A novel application of artificial neural network for recognition of target behind the wall," Microwave and Optical Technology Letters, Vol. 62, 152-167, 2020.
doi:10.1002/mop.32020

7. Singh, V., S. Bhattacharyya, and P. K. Jain, "Micro-Doppler classification of human movements using spectrogram spatial features and support vector machine," International Journal of RF and Microwave Computer-Aided Engineering, Vol. 30, e22264, 2020.

8. Celik, A. R. and M. B. Kurt, "Development of an ultra-wideband, stable and high-directive monopole disc antenna for radar-based microwave imaging of breast cancer," Journal of Microwave Power and Electromagnetic Energy, Vol. 52, 75-93, 2018.
doi:10.1080/08327823.2018.1458692

9. Cicchetti, R., V. Cicchetti, S. Costanzo, P. D'Atanasio, A. Fedeli, M. Pastorino, S. Pisa, E. Pittella, E. Piuzzi, C. Ponti, and A. Randazzo, "A microwave imaging system for the detection of targets hidden behind dielectric walls," 2020 XXXIIIrd General Assembly and Scientific Symposium of the International Union of Radio Science, 1-4, IEEE, 2020.

10. Nkwari, P. K. M., S. Sinha, and H. C. Ferreira, "Through-the-wall radar imaging: A review," IETE Technical Review, Vol. 35, 631-639, 2018.
doi:10.1080/02564602.2017.1364146

11. Narayanan, R. M., E. T. Gebhardt, and S. P. Broderick, "Through-wall single and multiple target imaging using MIMO radar," Electronics, Vol. 6, 70, 2017.
doi:10.3390/electronics6040070

12. Ralston, T. S., G. L. Charvat, and J. E. Peabody, "Real-time through-wall imaging using an ultrawideband multiple-input multiple-output (MIMO) phased array radar system," 2010 IEEE International Symposium on Phased Array Systems and Technology, 551-558, IEEE, October 2010.

13. Boudamouz, B., P. Millot, and C. Pichot, "Through the wall radar imaging with MIMO beamforming processing," 2011 Microwaves, Radar and Remote Sensing Symposium, 251-254, IEEE, August 2011.
doi:10.1109/MRRS.2011.6053647

14. Laviada, J., A. Arboleya, F. Lopez-Gayarre, and F. Las-Heras, "Broadband synthetic aperture scanning system for three-dimensional through-the-wall inspection," IEEE Geoscience and Remote Sensing Letters, Vol. 13, 97-101, 2015.
doi:10.1109/LGRS.2015.2498952

15. Ahmad, F., Y. Zhang, and M. G. Amin, "Three-dimensional wideband beamforming for imaging through a single wall," IEEE Geoscience and Remote Sensing Letters, Vol. 5, 176-179, 2008.
doi:10.1109/LGRS.2008.915742

16. Zhang, W. and A. Hoorfar, "Three-dimensional synthetic aperture radar imaging through multilayered walls," IEEE Transactions on Antennas and Propagation, Vol. 62, 459-462, 2013.
doi:10.1109/TAP.2013.2287274

17. Zhang, W. and A. Hoorfar, "Three-dimensional real-time through-the-wall radar imaging with diffraction tomographic algorithm," IEEE Transactions on Geoscience and Remote Sensing, Vol. 51, 4155-4163, 2012.
doi:10.1109/TGRS.2012.2227059

18. Yoon, Y. S. and M. G. Amin, "Compressed sensing technique for high-resolution radar imaging," Signal Processing, Sensor Fusion, and Target Recognition XVII, International Society for Optics and Photonics, Vol. 6968, 69681A, 2008.
doi:10.1117/12.777175

19. Song, Y., T. Jin, Y. Dai, Y. Song, and X. Zhou, "Through-wall human pose reconstruction via UWB MIMO radar and 3D CNN," Remote Sens., Vol. 13, 241, 2021.
doi:10.3390/rs13020241

20. Kilic, A., I. Babaoglu, A. Babalik, and A. Arslan, "Through-wall radar classification of human posture using convolutional neural networks," International Journal of Antennas and Propagation, 2019.

21. Zhu, C., E. A. Chan, Y. Wang, W. Peng, R. Guo, B. Zhang, C. Soci, and Y. Chong, "Image reconstruction through a multimode fiber with a simple neural network architecture," Scientific Reports, Vol. 11, 1-10, 2021.
doi:10.1038/s41598-020-79139-8

22. Skolnik, M., Introduction to Radar System, 3rd Ed., McGraw-Hill, 2017.

23. Gaikwad, A. N., D. Singh, and M. J. Nigam, "Application of clutter reduction techniques for detection of metallic and low dielectric target behind the brick wall by stepped frequency continuous wave radar in ultra-wideband range," IET Radar, Sonar & Navigation, Vol. 5, 416-425, 2011.
doi:10.1049/iet-rsn.2010.0059

24. Wang, G., M. G. Amin, and Y. Zhang, "New approach for target locations in the presence of wall ambiguities," IEEE Transactions on Aerospace and Electronic Systems, Vol. 42, 301-315, 2006.
doi:10.1109/TAES.2006.1603424

25. Ahmad, F. and M. G. Amin, "A noncoherent approach to radar localization through unknown walls," 2006 IEEE Conference on Radar, 2006.

26. Kaushal, S., B. Kumar, and D. Singh, "An autofocusing method for imaging the targets for TWI radar systems with correction of thickness and dielectric constant of wall," International Journal of Microwave and Wireless Technologies, Vol. 11, 15-21, 2019.
doi:10.1017/S1759078718001356

27. Protiva, P., J. Mrkvica, and J. Machac, "Estimation of wall parameters from time-delay-only through-wall radar measurements," IEEE Transactions on Antennas and Propagation, Vol. 59, 4268-4278, 2011.
doi:10.1109/TAP.2011.2164206

28. Singh, A. P., S. Dwivedi, and P. K. Jain, "A novel technique for contrast target detection in through-the-wall radar images," Journal of Electromagnetic Engineering and Sciences, Vol. 22, No. 3, 202-209, 2022.
doi:10.26866/jees.2022.3.r.78

29. Tivive, F. H. C., A. Bouzerdoum, and M. G. Amin, "A subspace projection approach for wall clutter mitigation in through-the-wall radar imaging," IEEE Transactions on Geoscience and Remote Sensing, Vol. 53, No. 4, 2108-2122, 2014.
doi:10.1109/TGRS.2014.2355211

30. Sekar, K., V. Duraisamy, and A. M. Remimol, "An approach of image scaling using DWT and bicubic interpolation," 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), 2014.

31. Singh, A. P., S. Dwivedi, and P. K. Jain, "Development of optimal thresholding technique for shape and size detection for through the wall radar imaging system," Defence Science Journal, Vol. 69, 564, 2019.
doi:10.14429/dsj.69.14574

32. Singh, D., N. K. Choudhary, K. C. Tiwari, and R. Prasad, "Shape recognition of shallow buried metallic objects at X-band using ANN and image analysis techniques," Progress In Electromagnetics Research B, Vol. 13, 257-273, 2009.
doi:10.2528/PIERB09010301

33. Gonzalez, R. C. and R. E. Woods, Digital Image Processing Using Matlab, 2nd Ed., Tata McGraw Hill, 2010.

34. Osowski, S., "Fourier and wavelet descriptors for shape recognition using neural networks --- A comparative study," Pattern Recognition, Vol. 35, 1949-1957, 2002.
doi:10.1016/S0031-3203(01)00153-4

35. Haykin, S., Neural Network --- A Comprehensive Foundation, 2nd Ed., Pearson Education, 2005.

36. Alwosheel, A., S. van Cranenburgh, and C. G. Chorus, "Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis," Journal of Choice Modelling, Vol. 28, 167-182, 2018.
doi:10.1016/j.jocm.2018.07.002

Dr. Akhilendra Pratap Singh