BibTex RIS Kaynak Göster
Yıl 2006, Cilt: 6 Sayı: 2, 157 - 168, 02.01.2012

Öz

Kaynakça

  • 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

Yıl 2006, Cilt: 6 Sayı: 2, 157 - 168, 02.01.2012

Öz

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

Kaynakça

  • 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.
Toplam 28 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Makaleler
Yazarlar

Engin Avcı Bu kişi benim

Ibrahim Turkoglu Bu kişi benim

Mustafa Poyraz Bu kişi benim

Yayımlanma Tarihi 2 Ocak 2012
Yayımlandığı Sayı Yıl 2006 Cilt: 6 Sayı: 2

Kaynak Göster

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. Ocak 2012;6(2):157-168.
Chicago Avcı, Engin, Ibrahim Turkoglu, ve 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, sy. 2 (Ocak 2012): 157-68.
EndNote Avcı E, Turkoglu I, Poyraz M (01 Ocak 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, ve 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, c. 6, sy. 2, ss. 157–168, 2012.
ISNAD Avcı, Engin vd. “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 (Ocak 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 vd. “A NEW APPROACH BASED ON WAVELET NERO GENETIC NETWORK FOR AUTOMATIC TARGET RECOGNITION WITH X-BAND DOPPLER RADAR”. IU-Journal of Electrical & Electronics Engineering, c. 6, sy. 2, 2012, ss. 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.