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Year 2006, Volume: 6 Issue: 2, 157 - 168, 02.01.2012

Abstract

References

  • J. Ahern, G. Y. Delisle, etc., Radar, LabVolt Ltd., vol. 1, p.p. 4-7 Canada, 1989.
  • Madrid J.J. M., Corredera J. R. C., “Vela G. M., A neural network approach to Dopplerbased target classification”, Radar 92. International Conference, pp. 450–453, Brighton, England, 1992.
  • Swiatnicki Z., Semklo R., “The artificial intelligence tools utilization in radar signal processing”, 12th International Conference on Microwaves and Radar (MIKON '98), vol. 3, pp. 799 –803, Krakow, Poland, 1998.
  • Mahafza, B., R., Radar Systems Analysis and Design Using, Chapman & Hall/CRC, United States of America, p.p.529, 2000.
  • Nelson, D., E., Starzyk, J., A., and Ensley D., D., IEEE Transaction on Systems, Man, And Cyberntics-Part A: Systems And Humans, Vol. 33, No. 1., 2002.
  • Davis, A., Marshak, A., and Wiscombe, W., “Wavelet –based Multifractal Analysis of NonStationary and/or Intermittent Geophysical Signals”, Wavelet in Geophysics, FoufoulaGeorgiou and Kumar (Editors), Academic Press, San Diego, 1994.
  • Grossmann, A., and Morlet., “Decomposition of Hardy Functions into Square Integrable Wavelets of Constant Shape”, SIAM J. Math. Analysis, 15(4), 723-736, 1984.
  • Murray, K., B., and Addison, P., S., “Wavelet Analysis of Shear Layer Flow Structures”, Proceedings of the 12th ASCE Engineering Mechanics Conference, La Jolla, California, May 17-20, 1998.
  • Kim, K., Choi, I., and Kim, “H. Efficient Radar Target Classification Using Adaptive Joint Time-Frequency Processing, IEEE Transactions on Antennas and Propagation”, Vol. 48, No. 12, 2000.
  • Tzanakou, E., M., Supervised And Unsupervised Pattern Recognition Feature Extraction and Computational, CRC Press LLC, 2000.
  • Shi, Y., and Zhang, “A Gabor Atom Network for Signal Classification With Application in Radar Target Recognition”, IEEE Transactions on Signal Processing, Vol. 49, No. 12, 2001.
  • Schreier, P., J., Scharf , L., L., “Stochastic Time-Frequency Analysis Using the Analytic Signal: Why the Complementary Distribution Matterts”, IEEE Transactions on Signal Processing, Vol.51, No.12.p.p. 3071 3079, 2003.
  • Turkoglu, I., Arslan, A. I., Erdoğan, “An expert system for diagnose of the heart valve diseases , Expert systems with applications” , 23(3) , 229-236, 2002.
  • Application of pattern recognition techniques for early warning radar, Nasa Technical Reports, AD-A299735, 1995.
  • Zhang, Q., “Using wavelet network in nonparametric estimation”, IEEE Trans. Neural Networks, vol. 8, pp. 227-236, 1997.
  • Roth, M., W., “Survey of neural network technology for automatic target recognition”, IEEE Trans. Neural Network, vol. 1, pp. 28-43, 1990.
  • Guangyi C., “Applications of wavelet transforms in pattern recognition and denoising”, Concordia University (Canada), 1999.
  • Sowelam S.M., Tewfik A.H., “Waveform selection in radar target classification”, IEEE Transactions on Information Theory, vol. 46, pp. 1014 –1029, 2000.
  • Kempen L.V., Sahli H., Nyssen E., etc., “Signal processing and pattern recognition methods for radar AP mine detection and identification”, Second International Conference on the Detection of Abandoned Land Mines, (Conf. Publ. No. 458), pp. 81–85, Edinburg, UK, 1998.
  • Noone G.P., “A neural approach to automatic pulse repetition interval modulation recognition”, Information Decision and Control, IDC 99 Proceedings, pp.213-218, Adelaide, Australia, 1999.
  • Roome S.J., “Classification of radar signals in modulation domain”, Electronics Letters, vol. 28, pp.704 –705, 1992.
  • Haykin, S., B., Currie, W. and Kesler, S., B., Maximum entropy spectral analysis of radar clutter, Proc. IEEE, vol. 70, No. 9 pp. 953-962, 1982.
  • Barbarossa, S. and Farina, A., “A Novel procedure for detection and focusing moving objects with SAR radar based on the Wigner-Ville Distribution”, Proc. Int. Radar Conf., pp. 44-47, 1990.
  • Duda R.O., Hart P.E., Pattern Classification and Scene Analysis, Stanford Research Institute, 1989.
  • Kil D.H., Shin F.B.,Pattern Recognition and Prediction with Applications to Signal Characterization, AIP Press, USA, 1996.
  • Turkoglu I., Arslan A., “Optimization of the Performance of Neural Network Based Pattern Recognition Classifiers with Distributed Systems, IEEE Computer Society, 2001 International Conference on Parallel and Distributed Systems (ICPADS’2001), pp. 379 382, Kyong Ju, Korea, Jun. 26-29, 2001.
  • Kulkarni, A., D., Computer Vision and Fuzzy-Neural Systems, Prentice Hall PTR, 2001.
  • Avci, E., Turkoglu, I., “Modeling of Tunnel Diode by Adaptive-Network-Based Fuzzy Inference System” , International Journal of Computational Intelligence , ISSN 1304-2386, Volume:1, Number:1 , 231-233, 2003.

A NEW APPROACH BASED ON WAVELET NERO GENETIC NETWORK FOR AUTOMATIC TARGET RECOGNITION WITH X-BAND DOPPLER RADAR

Year 2006, Volume: 6 Issue: 2, 157 - 168, 02.01.2012

Abstract

A NEW APPROACH BASED ON WAVELET NERO GENETIC NETWORK FOR AUTOMATIC TARGET RECOGNITION WITH X-BAND DOPPLER RADAR

References

  • J. Ahern, G. Y. Delisle, etc., Radar, LabVolt Ltd., vol. 1, p.p. 4-7 Canada, 1989.
  • Madrid J.J. M., Corredera J. R. C., “Vela G. M., A neural network approach to Dopplerbased target classification”, Radar 92. International Conference, pp. 450–453, Brighton, England, 1992.
  • Swiatnicki Z., Semklo R., “The artificial intelligence tools utilization in radar signal processing”, 12th International Conference on Microwaves and Radar (MIKON '98), vol. 3, pp. 799 –803, Krakow, Poland, 1998.
  • Mahafza, B., R., Radar Systems Analysis and Design Using, Chapman & Hall/CRC, United States of America, p.p.529, 2000.
  • Nelson, D., E., Starzyk, J., A., and Ensley D., D., IEEE Transaction on Systems, Man, And Cyberntics-Part A: Systems And Humans, Vol. 33, No. 1., 2002.
  • Davis, A., Marshak, A., and Wiscombe, W., “Wavelet –based Multifractal Analysis of NonStationary and/or Intermittent Geophysical Signals”, Wavelet in Geophysics, FoufoulaGeorgiou and Kumar (Editors), Academic Press, San Diego, 1994.
  • Grossmann, A., and Morlet., “Decomposition of Hardy Functions into Square Integrable Wavelets of Constant Shape”, SIAM J. Math. Analysis, 15(4), 723-736, 1984.
  • Murray, K., B., and Addison, P., S., “Wavelet Analysis of Shear Layer Flow Structures”, Proceedings of the 12th ASCE Engineering Mechanics Conference, La Jolla, California, May 17-20, 1998.
  • Kim, K., Choi, I., and Kim, “H. Efficient Radar Target Classification Using Adaptive Joint Time-Frequency Processing, IEEE Transactions on Antennas and Propagation”, Vol. 48, No. 12, 2000.
  • Tzanakou, E., M., Supervised And Unsupervised Pattern Recognition Feature Extraction and Computational, CRC Press LLC, 2000.
  • Shi, Y., and Zhang, “A Gabor Atom Network for Signal Classification With Application in Radar Target Recognition”, IEEE Transactions on Signal Processing, Vol. 49, No. 12, 2001.
  • Schreier, P., J., Scharf , L., L., “Stochastic Time-Frequency Analysis Using the Analytic Signal: Why the Complementary Distribution Matterts”, IEEE Transactions on Signal Processing, Vol.51, No.12.p.p. 3071 3079, 2003.
  • Turkoglu, I., Arslan, A. I., Erdoğan, “An expert system for diagnose of the heart valve diseases , Expert systems with applications” , 23(3) , 229-236, 2002.
  • Application of pattern recognition techniques for early warning radar, Nasa Technical Reports, AD-A299735, 1995.
  • Zhang, Q., “Using wavelet network in nonparametric estimation”, IEEE Trans. Neural Networks, vol. 8, pp. 227-236, 1997.
  • Roth, M., W., “Survey of neural network technology for automatic target recognition”, IEEE Trans. Neural Network, vol. 1, pp. 28-43, 1990.
  • Guangyi C., “Applications of wavelet transforms in pattern recognition and denoising”, Concordia University (Canada), 1999.
  • Sowelam S.M., Tewfik A.H., “Waveform selection in radar target classification”, IEEE Transactions on Information Theory, vol. 46, pp. 1014 –1029, 2000.
  • Kempen L.V., Sahli H., Nyssen E., etc., “Signal processing and pattern recognition methods for radar AP mine detection and identification”, Second International Conference on the Detection of Abandoned Land Mines, (Conf. Publ. No. 458), pp. 81–85, Edinburg, UK, 1998.
  • Noone G.P., “A neural approach to automatic pulse repetition interval modulation recognition”, Information Decision and Control, IDC 99 Proceedings, pp.213-218, Adelaide, Australia, 1999.
  • Roome S.J., “Classification of radar signals in modulation domain”, Electronics Letters, vol. 28, pp.704 –705, 1992.
  • Haykin, S., B., Currie, W. and Kesler, S., B., Maximum entropy spectral analysis of radar clutter, Proc. IEEE, vol. 70, No. 9 pp. 953-962, 1982.
  • Barbarossa, S. and Farina, A., “A Novel procedure for detection and focusing moving objects with SAR radar based on the Wigner-Ville Distribution”, Proc. Int. Radar Conf., pp. 44-47, 1990.
  • Duda R.O., Hart P.E., Pattern Classification and Scene Analysis, Stanford Research Institute, 1989.
  • Kil D.H., Shin F.B.,Pattern Recognition and Prediction with Applications to Signal Characterization, AIP Press, USA, 1996.
  • Turkoglu I., Arslan A., “Optimization of the Performance of Neural Network Based Pattern Recognition Classifiers with Distributed Systems, IEEE Computer Society, 2001 International Conference on Parallel and Distributed Systems (ICPADS’2001), pp. 379 382, Kyong Ju, Korea, Jun. 26-29, 2001.
  • Kulkarni, A., D., Computer Vision and Fuzzy-Neural Systems, Prentice Hall PTR, 2001.
  • Avci, E., Turkoglu, I., “Modeling of Tunnel Diode by Adaptive-Network-Based Fuzzy Inference System” , International Journal of Computational Intelligence , ISSN 1304-2386, Volume:1, Number:1 , 231-233, 2003.
There are 28 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Engin Avcı This is me

Ibrahim Turkoglu This is me

Mustafa Poyraz This is me

Publication Date January 2, 2012
Published in Issue Year 2006 Volume: 6 Issue: 2

Cite

APA Avcı, E., Turkoglu, I., & Poyraz, M. (2012). A NEW APPROACH BASED ON WAVELET NERO GENETIC NETWORK FOR AUTOMATIC TARGET RECOGNITION WITH X-BAND DOPPLER RADAR. IU-Journal of Electrical & Electronics Engineering, 6(2), 157-168.
AMA Avcı E, Turkoglu I, Poyraz M. A NEW APPROACH BASED ON WAVELET NERO GENETIC NETWORK FOR AUTOMATIC TARGET RECOGNITION WITH X-BAND DOPPLER RADAR. IU-Journal of Electrical & Electronics Engineering. January 2012;6(2):157-168.
Chicago Avcı, Engin, Ibrahim Turkoglu, and Mustafa Poyraz. “A NEW APPROACH BASED ON WAVELET NERO GENETIC NETWORK FOR AUTOMATIC TARGET RECOGNITION WITH X-BAND DOPPLER RADAR”. IU-Journal of Electrical & Electronics Engineering 6, no. 2 (January 2012): 157-68.
EndNote Avcı E, Turkoglu I, Poyraz M (January 1, 2012) A NEW APPROACH BASED ON WAVELET NERO GENETIC NETWORK FOR AUTOMATIC TARGET RECOGNITION WITH X-BAND DOPPLER RADAR. IU-Journal of Electrical & Electronics Engineering 6 2 157–168.
IEEE E. Avcı, I. Turkoglu, and M. Poyraz, “A NEW APPROACH BASED ON WAVELET NERO GENETIC NETWORK FOR AUTOMATIC TARGET RECOGNITION WITH X-BAND DOPPLER RADAR”, IU-Journal of Electrical & Electronics Engineering, vol. 6, no. 2, pp. 157–168, 2012.
ISNAD Avcı, Engin et al. “A NEW APPROACH BASED ON WAVELET NERO GENETIC NETWORK FOR AUTOMATIC TARGET RECOGNITION WITH X-BAND DOPPLER RADAR”. IU-Journal of Electrical & Electronics Engineering 6/2 (January 2012), 157-168.
JAMA Avcı E, Turkoglu I, Poyraz M. A NEW APPROACH BASED ON WAVELET NERO GENETIC NETWORK FOR AUTOMATIC TARGET RECOGNITION WITH X-BAND DOPPLER RADAR. IU-Journal of Electrical & Electronics Engineering. 2012;6:157–168.
MLA Avcı, Engin et al. “A NEW APPROACH BASED ON WAVELET NERO GENETIC NETWORK FOR AUTOMATIC TARGET RECOGNITION WITH X-BAND DOPPLER RADAR”. IU-Journal of Electrical & Electronics Engineering, vol. 6, no. 2, 2012, pp. 157-68.
Vancouver Avcı E, Turkoglu I, Poyraz M. A NEW APPROACH BASED ON WAVELET NERO GENETIC NETWORK FOR AUTOMATIC TARGET RECOGNITION WITH X-BAND DOPPLER RADAR. IU-Journal of Electrical & Electronics Engineering. 2012;6(2):157-68.