Ultrawideband (UWB) waveforms achieve excellent spatial resolution for better characterization of targets in tomographic imaging applications compared to narrowband waveforms. In this paper, two-dimensional tomographic images of multiple scattering objects are successfully obtained using the diffraction tomography approach by transmitting multiple independent and identically distributed (iid) UWB random noise waveforms. The feasibility of using a random noise waveform for tomography is investigated by formulating a white Gaussian noise (WGN) model using spectral estimation. The analytical formulation of object image formation using random noise waveforms is established based on the backward scattering, and several numerical diffraction tomography simulations are performed in the spatial frequency domain to validate the analytical results by reconstructing the tomographic images of scattering objects. The final image of the object based on multiple transmitted noise waveforms is reconstructed by averaging individually formed images which compares very well with the image created using the traditional Gaussian pulse. Pixel difference-based measure is used to analyze and estimate the image quality of the final reconstructed tomographic image under various signal-to-noise ratio (SNR) conditions. Also, preliminary experiment setup and measurement results are presented to assess the validation of simulation results. 1. Introduction Research on the use of random or pseudorandom noise transmit signals in radar has been conducted since the 1950s [1, 2]. Noise radar has been considered a promising technique for the covert identification of target objects due to several advantages, such as excellent electronic countermeasure (ECM), low probability of detection (LPD), low probability of interception (LPI) features, and relatively simple hardware architectures [3–5]. Also, advances in signal and imaging processing techniques in radar systems have progressed so that multidimensional representations of the target object can be obtained [6]. In general, radar imaging tends to be formulated in the time domain to exploit efficient back-projection algorithms, generate accurate shape features of the target object, and provide location data [7]. For multistatic radar systems, the images of a target are reconstructed based on range profiles obtained from the distributed sensor elements. When a transmitter radiates a waveform, spatially distributed receivers collect samples of the scattered field which are related to the electrical parameters of the target object. For the
References
[1]
B. M. Horton, “Noise-modulated distance measuring systems,” Proceedings of the IRE, vol. 47, no. 5, pp. 821–828, 1959.
[2]
M. P. Grant, G. R. Cooper, and A. K. Kamal, “A class of noise radar systems,” Proceedings of the IEEE, vol. 51, no. 7, pp. 1060–1061, 1963.
[3]
M. Dawood and R. M. Narayanan, “Multipath and ground clutter analysis for a UWB noise radar,” IEEE Transactions on Aerospace and Electronic Systems, vol. 38, no. 3, pp. 838–853, 2002.
[4]
K. Kulpa, J. Misiurewicz, Z. Gajo, and M. Malanowski, “A simple robust detection of weak target in noise radars,” in Proceedings of the 4th European Radar Conference (EURAD '07), pp. 275–278, Munich, Germany, October 2007.
[5]
Y. Zhang and R. M. Narayanan, “Design consideration for a real-time random-noise tracking radar,” IEEE Transactions on Aerospace and Electronic Systems, vol. 40, no. 2, pp. 434–445, 2004.
[6]
D. A. Ausherman, A. Kozma, J. L. Walker, H. M. Jones, and E. C. Poggio, “Developments in radar imaging,” IEEE Transactions on Aerospace and Electronic Systems, vol. 20, no. 4, pp. 363–400, 1984.
[7]
H. J. Shin, R. M. Narayanan, and M. Rangaswamy, “Tomographic imaging with ultra-wideband noise radar using time-domain data,” in Radar Sensor Technology XVII, vol. 8714 of Proceedings of SPIE, pp. 1–9, Baltimore, Md, USA, April 2013.
[8]
H. J. Shin, R. M. Narayanan, and M. Rangaswamy, “Diffraction tomography for ultra-wideband noise radar and imaging quality measure of a cylindrical perfectly conducting object,” in Proceedings of the 2014 IEEE Radar Conference, pp. 702–707, Cincinnati, Ohio, USA, May 2014.
[9]
L. Jofre, A. Broquetas, J. Romeu et al., “UWB tomographie radar imaging of penetrable and impenetrable objects,” Proceedings of the IEEE, vol. 97, no. 2, pp. 451–464, 2009.
[10]
X. Li, E. J. Bond, B. D. van Veen, and S. C. Hagness, “An overview of ultra-wideband microwave imaging via space-time beamforming for early-stage breast-cancer detection,” IEEE Antennas and Propagation Magazine, vol. 47, no. 1, pp. 19–34, 2005.
[11]
T. M. Grzegorczyk, P. M. Meaney, P. A. Kaufman, R. M. Diflorio-Alexander, and K. D. Paulsen, “Fast 3-D tomographic microwave imaging for breast cancer detection,” IEEE Transactions on Medical Imaging, vol. 31, no. 8, pp. 1584–1592, 2012.
[12]
M. H. Khalil, W. Shahzad, and J. D. Xu, “In the medical field detection of breast cancer by microwave imaging is a robust tool,” in Proceedings of the 25th International Vacuum Nanoelectronics Conference (IVNC '12), pp. 228–229, Jeju Island, Republic of Korea, July 2012.
[13]
Z. Wang, E. G. Lim, Y. Tang, and M. Leach, “Medical applications of microwave imaging,” The Scientific World Journal, vol. 2014, Article ID 147016, 7 pages, 2014.
[14]
Y. J. Kim, L. Jofre, F. De Flaviis, and M. Q. Feng, “Microwave reflection tomographic array for damage detection of civil structures,” IEEE Transactions on Antennas and Propagation, vol. 51, no. 11, pp. 3022–3032, 2003.
[15]
S. Kharkovsky, J. Case, M. Ghasr, R. Zoughi, S. Bae, and A. Belarbi, “Application of microwave 3D SAR imaging technique for evaluation of corrosion in steel rebars embedded in cement-based structures,” in Review of Progress in Quantitative Nondestructive Evaluation, vol. 31, pp. 1516–1523, 2012.
[16]
O. Güne？ and O. Büyük？ztürk, “Microwave imaging of plain and reinforced concrete for NDT using backpropagation algorithm,” in Nondestructive Testing of Materials and Structures, O. Güne？ and Y. Akkaya, Eds., vol. 6 of RILEM Bookseries, pp. 703–709, Springer, 2013.
[17]
D. Zimdars and J. S. White, “Terahertz reflection imaging for package and personnel inspection,” in Terahertz for Military and Security Applications II, vol. 5411 of Proceedings of the SPIE, pp. 78–83, Orlando, Fla, USA, April 2004.
[18]
S. Almazroui and W. Wang, “Microwave tomography for security applications,” in Proceedings of the International Conference on Information Technology and e-Services (ICITeS '12), pp. 1–3, Sousse, Tunisia, March 2012.
[19]
O. Yurduseven, “Indirect microwave holographic imaging of concealed ordnance for airport security imaging systems,” Progress in Electromagnetics Research, vol. 146, pp. 7–13, 2014.
[20]
L. Jofre, A. P. Toda, J. M. J. Montana et al., “UWB short-range bifocusing tomographic imaging,” IEEE Transactions on Instrumentation and Measurement, vol. 57, no. 11, pp. 2414–2420, 2008.
[21]
C.-P. Lai and R. M. Narayanan, “Ultrawideband random noise radar design for through-wall surveillance,” IEEE Transactions on Aerospace and Electronic Systems, vol. 46, no. 4, pp. 1716–1730, 2010.
[22]
R. Vela, R. M. Narayanan, K. A. Gallagher, and M. Rangaswamy, “Noise radar tomography,” in Proceedings of the IEEE Radar Conference: Ubiquitous Radar (RADAR '12), pp. 720–724, Atlanta, Ga, USA, May 2012.
[23]
A. Leon-Garcia, Probability and Random Processes for Electrical Engineering, Addison-Wesley, Reading, Mass, USA, 2nd edition, 1994.
[24]
G. Jenkins and D. Watts, Watts, Spectral Analysis and Its Applications, Holden-Day, San Francisco, Calif, USA, 1968.
[25]
A. V. Oppenheim, R. W. Schafer, and J. R. Buck, Discrete-Time Signal Processing, Prentice Hall, Upper Saddle River, NJ, USA, 2nd edition, 1999.
[26]
M. S. Bartlett, “Periodogram analysis and continuous spectra,” Biometrika, vol. 37, pp. 1–16, 1950.
[27]
S. K. Kenue and J. F. Greenleaf, “Limited angle multifrequency diffraction tomography,” IEEE Transactions on Sonics and Ultrasonics, vol. 29, no. 4, pp. 213–217, 1982.
[28]
B. A. Roberts and A. C. Kak, “Reflection mode diffraction tomography,” Ultrasonic Imaging, vol. 7, no. 4, pp. 300–320, 1985.
[29]
M. Soumekh, “Surface imaging via wave equation inversion,” in Acoustical Imaging, L. W. Kessler, Ed., pp. 383–393, Springer, 1988.
[30]
S. X. Pan and A. C. Kak, “A computational study of reconstruction algorithms for diffraction tomography: interpolation versus filtered-backpropagation,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 31, no. 5, pp. 1262–1275, 1983.
[31]
T.-H. Chu and K.-Y. Lee, “Wide-band microwave diffraction tomography under Born approximation,” IEEE Transactions on Antennas and Propagation, vol. 37, pp. 515–519, 1992.
[32]
J. C. Bolomey and C. Pichot, “Microwave tomography: from theory to practical imaging systems,” International Journal of Imaging Systems and Technology, vol. 2, pp. 144–156, 1990.
[33]
H. J. Shin, R. M. Narayanan, and M. Rangaswamy, “Ultra-wideband noise radar imaging of cylindrical PEC objects using diffraction tomography,” in Radar Sensor Technology XVIII, vol. 9077 of Proceedings of SPIE, pp. 1–10, Baltimore, Md, USA, May 2014.
[34]
I. Avciba？, B. Sankur, and K. Sayood, “Statistical evaluation of image quality measures,” Journal of Electronic Imaging, vol. 11, no. 2, pp. 206–223, 2002.
[35]
A. M. Eskicioglu and P. S. Fisher, “Image quality measures and their performance,” IEEE Transactions on Communications, vol. 43, no. 12, pp. 2959–2965, 1995.
[36]
A. M. Eskicioglu, “Application of multidimensional quality measures to reconstructed medical images,” Optical Engineering, vol. 35, no. 3, pp. 778–785, 1996.
[37]
J. Hu, T. Jiang, Z. Cui, and Y. Hou, “Design of UWB pulses based on Gaussian pulse,” in Proceedings of the 3rd IEEE International Conference on Nano/Micro Engineered and Molecular Systems (NEMS '08), pp. 651–655, Sanya, China, January 2008.
[38]
A. Thakre and A. Dhenge, “Selection of pulse for ultra wide band communication (UWB) system,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 1, pp. 683–686, 2012.
[39]
C. E. Shannon, “Communication in the presence of noise,” Proceedings of the IRE, vol. 37, pp. 10–21, 1949.
[40]
M. Bertero and P. Boccacci, Introduction to Inverse Problems in Imaging, Institute of Physics Publishing, Bristol, UK, 1998.
