volume | PIER Journals

Abstract

Through-the-Wall Imaging systems are a promising method for on-line applications,especially in disaster areas, where victims are buried under collapsed walls. These applications require such systems to identify the shape of thetarget. The foremost step while performing the task of shape recognition of stationary targets behind a wall is to first detect the target position, its approximate shape and size, and then, subsequent processing of these images with the use of signal processing techniques for the shape recognition of targets. For determining highly accurate information about target location and its approximate shape, a high-resolution image of the target is required. In literature, various imaging algorithms have been reported, some of which are back projection, delay sum, and frequency-wavenumber imaging algorithm. However, the use of these algorithms for shape detection of the target has not been explored so far. Therefore, it becomes essential to explore the use of these algorithms on TWI data to select an effective imaging algorithm for detecting approximate shape and size of the target. For this purpose, an experiment has been performed. The performances of these imaging algorithms have been analyzed and evaluated. The detected target images do not correspond to the actual shape and size of targets; therefore, a novel methodology using an artificial neural network has been presented for predicting the actual shape of the target. From the experimental data, the retrieved result of shape has been found in good agreement with the target original shape.

1. Baranoski, E. J., "Through-wall imaging: Historical perspective and future directions," Journal of the Franklin Institute, Vol. 345, 556-569, 2008.
doi:10.1016/j.jfranklin.2008.01.005 Google Scholar

2. Smith, G. E. and B. G. Mobasseri, "Robust through-the-wall radar image classification using a target-model alignment procedure," IEEE Transactions on Image Processing, Vol. 21, 754-767, 2011.
doi:10.1109/TIP.2011.2166967 Google Scholar

3. Hantscher, S., B. Praher, A. Reisenzahn, and C. G. Diskus, "Comparison of UWB target identification algorithms for through-wall imaging applications," IEEE European Radar Conference, 104-107, 2006. Google Scholar

4. Kidera, S., T. Sakamoto, and T. Sato, "High-resolution 3-D imaging algorithm with an envelope of modified spheres for UWB through-the-wall radars," IEEE Transactions on Antennas and Propagation, Vol. 57, 3520-3529, 2009.
doi:10.1109/TAP.2009.2032337 Google Scholar

5. Dogaru, T. and C. Le, Through-the-wall Small Weapon Detection Based on Polarimetric Radar Techniques, Army Research Lab Adelphi MD Sensors and Electronic Devices Directorate, No. ARLTR-5041, 2009.

6. Mirbach, M. and W. Menzel, "A simple surface estimation algorithm for UWB pulse radars based on trilateration," IEEE International Conference on Ultra-Wideband (ICUWB), 273-277, 2011.
doi:10.1109/ICUWB.2011.6058844 Google Scholar

7. Wu, S., Y. Xu, J. Chen, S. Meng, G. Fang, and H. Yin, "Through-wall shape estimation based on UWB-SP radar," IEEE Geoscience and Remote Sensing Letters, Vol. 10, 1234-1238, 2013.
doi:10.1109/LGRS.2013.2260128 Google Scholar

8. Dehmollaian, M., "Through-wall shape reconstruction and wall parameters estimation using differential evolution," IEEE Geoscience and Remote Sensing Letters, Vol. 8, 201-205, 2010.
doi:10.1109/LGRS.2010.2056912 Google Scholar

9. Ahmad, F., M. G. Amin, and S. A. Kassam, "Synthetic aperture beamformer for imaging through a dielectric wall," IEEE Transactions on Aerospace and Electronic Systems, Vol. 41, 271-283, 2005.
doi:10.1109/TAES.2005.1413761 Google Scholar

10. Hunt, A. R., "Use of a frequency-hopping radar for imaging and motion detection through walls," IEEE Transactions on Geoscience and Remote Sensing, Vol. 47, 1402-1408, 2009.
doi:10.1109/TGRS.2009.2016084 Google Scholar

11. 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 Google Scholar

12. Hantscher, S., B. Praher, A. Reisenzahn, and C. G. Diskus, "Analysis of imaging radar algorithms for the identification of targets by their surface shape," Int. Conf. on UWB, 2006. Google Scholar

13. Yigit, E., S. Demirci, C. Ozdemir, and A. Kavak, "A synthetic aperture radar-based focusing algorithm for B-scan ground penetrating radar imagery," Microwave and Optical Technology Letters, Vol. 49, 2534-2540, 2007.
doi:10.1002/mop.22724 Google Scholar

14. Ozdemir, C., S. Demirci, and E. Yigit, "Practical algorithms to focus B-scan GPR images: Theory and application to real data," Progress In Electromagnetics Research B, Vol. 6, 109-122, 2008.
doi:10.2528/PIERB08031207 Google Scholar

15. Verma, P. K., A. N. Gaikwad, D. Singh, and M. J. Nigam, "Analysis of clutter reduction techniques for through wall imaging in UWB range," Progress In Electromagnetics Research B, Vol. 17, 29-48, 2009.
doi:10.2528/PIERB09060903 Google Scholar

16. Muqaibel, A. H. and A. Safaai-Jazi, "A new formulation for characterization of materials based on measured insertion transfer function," IEEE Transactions on Microwave Theory and Techniques, Vol. 51, 1946-1951, 2003.
doi:10.1109/TMTT.2003.815274 Google Scholar

17. Chandra, R., A. N. Gaikwad, D. Singh, and M. J. Nigam, "An approach to remove the clutter and detect the target for ultra-wideband through-wall imaging," Journal of Geophysics and Engineering, Vol. 5, 412-419, 2008.
doi:10.1088/1742-2132/5/4/005 Google Scholar

18. 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 Google Scholar

19. Ibrahim, K. M., K. F. A. Hussein, and A.-E.-H. A.-E.-A. Ammar, "Land-buried object detection and target-shape recognition in lossy and dispersive soil," Progress In Electromagnetics Research B, Vol. 57, 279-298, 2014.
doi:10.2528/PIERB13111303 Google Scholar

20. Kumar, B., R. Upadhyay, and D. Singh, "Development of an adaptive approach for identification of targets (match box, pocket diary and cigarette box) under the cloth with MMW imaging system," Progress In Electromagnetics Research B, Vol. 77, 37-55, 2017.
doi:10.2528/PIERB17040804 Google Scholar

21. Easyfit by mathwave technologies, [Online]. Available:http://www.mathwave.com/easyfitdistribution-fitting.html.

22. Forbes, C., E. Merran, H. Nicholus, and P. Brian, Statistical Distributions, John Wiley and Sons, 2011.

23. Gonzalez, S. and W. Richards, Digital Image Processing, Dorling Kindersley, 2009.

24. 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 Google Scholar

25. Haykin, S., Neural Network a Comprehensive Foundation, Pearson Education, 2005.

Dr. Akhilendra Pratap Singh

Dr. Smrity Dwivedi

Dr. Pradip Kumar Jain