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An Improved Decision Fusion Technique to Increase the Performance Level of Hrr ATR Systems

By Iulian Constantin Vizitiu
Progress In Electromagnetics Research, Vol. 139, 87-104, 2013


According to literature, a significant and up to date research direction to increase the performance level of automatic target recognition (ATR) systems is focused on the use of information coming from an appropriate set of EM sensors and high-quality decision fusion techniques, respectively. Consequently, in this paper a genetic optimized version of Sugeno's fuzzy integral is discussed. In addition, using a real database belonging to the high-resolution radar (HRR) imagery, the superiority of the proposed decision fusion technique related to its standard version and other well-known decision fusion methods is also demonstrated.


Iulian Constantin Vizitiu, "An Improved Decision Fusion Technique to Increase the Performance Level of Hrr ATR Systems," Progress In Electromagnetics Research, Vol. 139, 87-104, 2013.


    1. Outhouse, M., A. Beach, and A. Parslow, "Automatic target recognition," Proceedings of SEAS DTC Technical Conference, Vol. A2, 2010.

    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.

    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.

    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.

    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.

    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.

    15. Martin, A. and E. Radoi, "Effective ATR algorithms using information fusion models," Proceedings of International Conference on Information Fusion, 161-166, 2004.

    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.

    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.

    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.

    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.

    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.

    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.

    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.