Department of Military Communications and Electronic Systems
Military Technical Academy
Romania
Homepage1. Outhouse, M., A. Beach, and A. Parslow, "Automatic target recognition," Proceedings of SEAS DTC Technical Conference, Vol. A2, 2010. Google Scholar
2. Nebabib, V. G., Methods and Techniques of Radar Recognition, Artech House, London, 1994.
3. Tait, P., Introduction to Radar Target Recognition, IET Radar and Sonar Navigation Series, No. 18, 2005.
4. Duda, R. O., P. O. Hart, and D. G. Stork, Pattern Classification, John Wiley & Sons, New York, 2001.
5. Lee, C. K., 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.
doi:10.2528/PIER08010206 Google Scholar
6. Park, S. H., J. H. Lee, and K. T. Kim, "Performance analysis of the scenario-based construction method for real target ISAR recognition," Progress In Electromagnetics Research, Vol. 128, 137-151, 2012. Google Scholar
7. Quinquis, A., E. Radoi, and F. Totir, "Some radar imagery results using superresolution techniques," IEEE Tran. on Antennas and Propagation, Vol. 52, No. 5, 1-15, 2004. Google Scholar
8. Anton, L., Signal Processing in High Resolution Radars, MTA Press, Bucharest, 2008.
9. Han, S. K., H. T. Kim, and S. H. Park, "Efficient radar target recognition using a combination of range profile and time-frequency analysis," Progress In Electromagnetics Research, Vol. 108, 131-140, 2010.
doi:10.2528/PIER10071601 Google Scholar
10. Hall, D. L. and J. Llinas, Handbook of Multisensor Data Fusion, CRC Press, Boca Raton, 2001.
11. Vizitiu, I. C., Fundamentals of Electronic Warfare, MatrixRom, Bucharest, 2011.
12. Molder, C., Pattern Recognition: Classification Algorithms, MTA Press, Bucharest, 2004.
13. Ng, G. W., Intelligent Systems-fusion, Tracking and Control, RSP Press, Hertfordshire, 2003.
14. Mangai, U. G., S. Samanta, and P. R. Chowdhury, "A survey of decision fusion and feature fusion strategies for pattern classification," IETE Technical Review, Vol. 27, 293-307, 2010.
doi:10.4103/0256-4602.64604 Google Scholar
15. Martin, A. and E. Radoi, "Effective ATR algorithms using information fusion models," Proceedings of International Conference on Information Fusion, 161-166, 2004. Google Scholar
16. Vizitiu, I. C. and I. Nicolaescu, "More efficient ATR system using the decision fusion between HRR and video imageries," Proceedings of IEEE International MRRS Conference, 272-275, 2011. Google Scholar
17. Alam, H., R. Hartono, and M. Fairhurst, "Use of genetic algorithms for optimizing a decision fusion framework," Proceedings of IEEE International Conference on Information Fusion, 831-837, 2003. Google Scholar
18. Vizitiu, I. C., Neuro-fuzzy-genetic Architectures: Theory and Applications, MTA Press, Bucharest, 2011.
19. Cui, M., Genetic Algorithms Based Feature Selection and Decision Fusion for Robust Remote Sensing Image Analysis, ProQuest, Cambridge, 2012.
20. Eiben, A. E. and J. E. Smith, Introduction to Evolutionary Computing, Springer, Berlin, 2008.
doi:10.1007/978-3-662-05094-1
21. Roy, R. and T. Kailath, "ESPRIT-estimation of signal parameters via rotational invariance techniques," IEEE Tran. on Acoustic Speech, Signal Processing, Vol. 37, 984-995, 1986. Google Scholar
22. Radoi, E., A. Quinquis, and F. Totir, "Superresolution ISAR image classification using Fourier descriptors and SART neural network," Proceedings of the European Conference on Synthetic Aperture Radar, 241-244, 2004. Google Scholar
23. Carpenter, G., S. Grossberg, and J. H. Reynolds, "ARTMAP: Supervised real-time learning and classification of nonstationary data by a self-organizing neural network," Neural Networks, Vol. 4, 565-588, 1991.
doi:10.1016/0893-6080(91)90012-T Google Scholar
24. Radoi, E., A. Quinquis, and F. Totir, "Supervised self-organizing classification of superresolution ISAR images: An anechoic chamber experiment," EURASIP Journal on Applied Signal Processing, Vol. 2006, 1-14, 2006. Google Scholar
