Non-Cooperative Target Recognition (NCTR) of aircrafts from radar measurements is a formidable problem that has drawn the attention of engineers and scientists over the last years. NCTR techniques typically involve a database with a huge amount of information from different known targets and a reliable identification algorithm able to highlight the likeness between measured and stored data. This paper uses High Resolution Range Profiles produced with a high-frequency software tool to train Articial Neural Networks for distinguishing between different classes of aircrafts. Actual data from the ORFEO measurement campaign are used to assess the performance of the trained networks.
2. Cohen, M. N., "An overview of radar-based automatic non cooperative target recognition techniques," IEEE International Conference on Systems Engineering, 29-34, 1991.
3. Rosenbach, R. and J. Schiller, Non-cooperative air target identi¯cation using radar imagery: indentification rate as a function of signal bandwidth, IEEE International Radar Conference, 305-309, 2000.
4. Conde, O. M., J. Perez, and M. F. Catedra, "Stationary phase method application for the analysis of radiation of complex 3d conducting structures," IEEE Transactions on Antennas and Propagation, Vol. 49, No. 5, 724-731, 2001.
5. Wehner, R. D., High Resolution Radar, 2nd Ed., Artech House, 1995.
6. Heiden, R., Aircraft recognition with radar range profiles, Ph.D. Thesis, University of Amsterdam, The Netherlands, 1998.
7. Hsueh-Jyh, L., "Using range profiles as feature vectors to identify aerospace objects," IEEE Transactions on Antennas and Propagation, Vol. 41, No. 3, 261-268, 1993.
8. Zyweck, A. and R. E. Bogner, "Radar target classification of commercial aircraft," IEEE Transactions on Aerospace and Electronic Systems, Vol. 32, No. 2, 598-606, 1996.
9. Liu, J., J. Zhang, and F. Zhao, "Feature for distinguishing propeller-driven airplanes from turbine-driven airplanes," IEEE Transactions on Aerospace and Electronic Systems, Vol. 46, No. 1, 222-229, 2010.
10. Lee, K. C., C. W. Huang, and M. C. Fang, "Radar target recognition by projected features of frequency-diversity RCS," Progress In Electromagnetics Research, Vol. 81, 121-133, 2008.
11. Han, S. K. and H. T. Kim, "Efficent radar target recognition of range profile and time-frequency analysis," Progress In Electromagnetics Research, Vol. 108, 131-140, 2010.
12. Roth, M. W., "Survey of neural network technology for automatic target recognition," IEEE Transactions on Neural Networks, Vol. 1, No. 1, 28-43, 1990.
13. Jouny, I., F. D. Garber, and S. C. Ahalt, "Classification of radar targets using synthetic neural networks," IEEE Transactions on Aerospace and Electronic Systems, Vol. 29, No. 2, 336-344, 1993.
14. Heiden, R. and J. Vries, TheORFEOMeasurementCampaign, TNO Defence, Security and Safety Report, FELSTAR-96-A073, 1996.
15. Farin, G., Curves and Surfaces for CAGD.: A Practical Guide, Morgan Kaufman Publishers, 2002.
16. Montiel, I., D. Poyatos, I. Gonzalez, D. Escot, C. Garcia, and E. Diego, FASCRO code and the synthetic database generation problem, Proc. of SET-080 Target Identification and Recognition Using RF System, Oslo, Norway, 2004.
17. Escot-Bocanegra, D., D. Poyatos-Martinez, R. Fernandez-Recio, A. Jurado-Lucena, and I. Montiel-Sanchez, "New bench-mark radar targets for scattering analysis and electromagnetic software validation," Progress In Electromagnetics Research, Vol. 88, 39-52, 2008.