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AN INTELLIGENT TARGET RECOGNITION SYSTEM BASED ON PERIODOGRAM FOR PULSED RADAR SYSTEMSPERIODOGRAM FOR PULSED RADAR SYSTEMS

Year 2005, Volume: 18 Issue: 2, 259 - 272, 13.08.2010

Abstract

ABSTRACT

In this study, a pattern recognition system is developed for automatic classification of the radar target signals. For feature extraction which is an important subset of the pattern recognition system, a new method which is based on periodogram power spectral density and intelligent classifier is proposed. Artificial neural network and adaptive network based fuzzy inference system were used as an intelligent classifier respectively. Radar signals were obtained from pulse radar system for various targets. According to developed feature extraction method, the classifier performances were evaluated with radar signals on the target recognition.

References

  • Ahern J., Delisle G. Y., etc. Radar, Lab-Volt Ltd., vol. 1, Canada, (1989).
  • Madrid J.J. M., Corredera J. R. C., Vela G. M., “A neural network approach to Doppler-based target classification”, Radar 92. International Conference, Brighton, England , 450–453, (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, Krakow, Poland, 799 –803, (1998).
  • Jakubiak A., Arabas J., Grabczak K., etc., “Radar clutter classification using Kohonen neural network”, Radar 97 (Conf. Publ. No. 449), Edinburgh , UK, 185 –188, (1997).
  • Tang B., Jiang W., Ke Y., “Radar signal classification by projection onto wavelet packet subspaces”, CIE International Conference of Radar Proceedings, Beijing, China, 124–126, (1996).
  • Beastall W. D.,” Recognition of radar signals by neural network”, First IEE International Conference on Artificial Neural Networks, (Conf. Publ. No. 313), London, UK, 139-142, (1989).
  • Application of pattern recognition techniques for early warning radar, Nasa Technical Reports, AD-A299735, Mar , (1995).
  • Guangyi C., “Applications of wavelet transforms in pattern recognition and de-noising”, Concordia University (Canada), (1999).
  • Sowelam S.M., Tewfik A.H., “Waveform selection in radar target classification”, IEEE Transactions on Information Theory, Vol. 46, 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), Edinburg, UK, 81–85, (1998).
  • Noone G.P., “A neural approach to automatic pulse repetition interval modulation recognition”, Information Decision and Control, IDC 99 Proceedings, Adelaide, Australia, 213-218, (1999).
  • Zyweck A., Bogner R.E, “Radar target recognition using range profiles”, IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP-94., vol. 2, Adelaide, Australia, II/373 -II/376, (1994).
  • Roome S.J.,”Classification of radar signals in modulation domain”, Electronics Letters, vol.28, 704 –705, (1992).
  • Liu J., Gao S., Luo Z.Q., etc., “The minimum description length criterion applied to emitter number detection and pulse classification, Statistical Signal and Array Processing”, Proceedings., Ninth IEEE SP Workshop, Portland, Oregon, USA, 172 –175, (1998).
  • Richards M. A., Fundamentals of Radar Signal Processing, Georgia Institute of Technology, (2000).
  • Ahern J., Delisle G. Y., etc. Radar, Lab-Volt Ltd., vol. 2, Canada, (1990).
  • Rothe H., Approaches to Pattern Recognition, Advanced Pattern Recognition Techniques, NATO-RTO Lecture Series 214, Lisbon Portugal, 1-1, 1-29. (1998).
  • Duda R.O., Hart P.E., “Pattern Classification and Scene Analysis”, Stanford Research Institute, (1989).
  • Bishop C.M., Neural Networks for Pattern Recognition, Clarendon Press, Oxford, (1996).
  • Kil D.H., Shin F.B.,”Pattern Recognition and Prediction with Applications to Signal Characterization”, AIP Press, USA, (1996).
  • Turkoglu, I., “Automatic Target Distance Recognition by Using Continuous Wave Radar Doppler Signals and Artificial Neural Networks”, DAUM, 1, 80-87, Elazig, (2002).
  • Turkoglu, I., “An Intelligent Pattern Recognition for Non-stationary Signals Based on The Time – Frequency Entropies”, PhD Thesis, Firat University Graduate School of Natural and Applied Sciences, Elazig, 40-47, (2002).
  • Percival D. B., Walden A. T., “Spectral analysis for physical applications:Multitaper and conventional univariate techniques”, Cambridge University Press., New York (1993).
  • Lin C. T., Lee C.S.G., “Neural Fuzzy Systems”, Prentice-Hall, (1996).
  • Zurada, M.J., “Introduction to Artificial Neural Systems”, West Publishing Company Inc., New York, (1992).
  • Turkoglu, I., Arslan A., “Power spectrum of the AR model and feature extraction method based on artificial neural network for target classification”, Gazi University, Technical Education Faculty, Journal of Polytechnic , 5(2) , 121- , May, (2002).
  • Kara S., “Doppler cihazı ve autoregresif spektral analiz metoduyla mitral ve triküspit kapaklardaki kan akışının incelenmesi”, Doktora tezi, Erciyes Üniversitesi Fen Bilimleri Enstitüsü, 15-20, (1994).
  • Yüksel M. E., “Ultrasonik Doppler İşaretlerinin Bilgisayar Destekli Analizi”, Y. Lisans tezi, Erciyes Üniversitesi Fen Bilimleri Enstitüsü, 25-43, (1993).
  • Turkoglu I., Arslan A., “Optimisation of the Performance of Neural Network Based Pattern Recognition Classifiers with Distributed Systems”, IEEE Computer Society, International Conference on Parallel and Distributed Systems (ICPADS’2001), ,Kyong Ju, Korea, 379-382 (2001).
  • Kosko, B., Neural Networks and Fuzzy Systems, A Dynamical Systems Approach, Englewood Ciffs., NJ: Prentice Hall, (1991).
  • Jang, J. S. R., Sun, C. T., “Neuro-Fuzzy Modeling and Control”, Proceedings of the IEEE, vol. 83, No. 3, (1995).
  • Jang, J. S. R., May., “ANFIS: Adaptive network-based fuzzy inference systems”, IEEE Trans. Syst., Man. and Cybern., vol. 23, 665-685., (1993).

DARBELİ RADAR SİSTEMLERİNDE GELİŞTİRİLEN PERİODOGRAM ÇIKARIMLI AKILLI HEDEF TANIMA

Year 2005, Volume: 18 Issue: 2, 259 - 272, 13.08.2010

Abstract

Bu çalışmada, radar hedef işaretlerini sınıflamak üzere akıllı örüntü tanıma sistemi geliştirilmiştir. Örüntü tanımanın önemli bir kısmı olan özellik çıkarma için periodogram güç spektrum yoğunluğu ve akıllı sınıflandırıcı temelli bir yöntem sunulmuştur. Akıllı sınıflandırıcı olarak, yapay sinir ağı ve uyarlamalı yapay sinir ağı temelli bulanık çıkarım sınıflandırıcısı kullanılmıştır. Radar işaretleri, farklı hedefler için darbeli radar sisteminden elde edilmiştir. Her iki sınıflandırıcının başarımları, geliştirilen özellik çıkarma yöntemine göre radar işaretleri ile hedef tanımada değerlendirilmiştir

References

  • Ahern J., Delisle G. Y., etc. Radar, Lab-Volt Ltd., vol. 1, Canada, (1989).
  • Madrid J.J. M., Corredera J. R. C., Vela G. M., “A neural network approach to Doppler-based target classification”, Radar 92. International Conference, Brighton, England , 450–453, (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, Krakow, Poland, 799 –803, (1998).
  • Jakubiak A., Arabas J., Grabczak K., etc., “Radar clutter classification using Kohonen neural network”, Radar 97 (Conf. Publ. No. 449), Edinburgh , UK, 185 –188, (1997).
  • Tang B., Jiang W., Ke Y., “Radar signal classification by projection onto wavelet packet subspaces”, CIE International Conference of Radar Proceedings, Beijing, China, 124–126, (1996).
  • Beastall W. D.,” Recognition of radar signals by neural network”, First IEE International Conference on Artificial Neural Networks, (Conf. Publ. No. 313), London, UK, 139-142, (1989).
  • Application of pattern recognition techniques for early warning radar, Nasa Technical Reports, AD-A299735, Mar , (1995).
  • Guangyi C., “Applications of wavelet transforms in pattern recognition and de-noising”, Concordia University (Canada), (1999).
  • Sowelam S.M., Tewfik A.H., “Waveform selection in radar target classification”, IEEE Transactions on Information Theory, Vol. 46, 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), Edinburg, UK, 81–85, (1998).
  • Noone G.P., “A neural approach to automatic pulse repetition interval modulation recognition”, Information Decision and Control, IDC 99 Proceedings, Adelaide, Australia, 213-218, (1999).
  • Zyweck A., Bogner R.E, “Radar target recognition using range profiles”, IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP-94., vol. 2, Adelaide, Australia, II/373 -II/376, (1994).
  • Roome S.J.,”Classification of radar signals in modulation domain”, Electronics Letters, vol.28, 704 –705, (1992).
  • Liu J., Gao S., Luo Z.Q., etc., “The minimum description length criterion applied to emitter number detection and pulse classification, Statistical Signal and Array Processing”, Proceedings., Ninth IEEE SP Workshop, Portland, Oregon, USA, 172 –175, (1998).
  • Richards M. A., Fundamentals of Radar Signal Processing, Georgia Institute of Technology, (2000).
  • Ahern J., Delisle G. Y., etc. Radar, Lab-Volt Ltd., vol. 2, Canada, (1990).
  • Rothe H., Approaches to Pattern Recognition, Advanced Pattern Recognition Techniques, NATO-RTO Lecture Series 214, Lisbon Portugal, 1-1, 1-29. (1998).
  • Duda R.O., Hart P.E., “Pattern Classification and Scene Analysis”, Stanford Research Institute, (1989).
  • Bishop C.M., Neural Networks for Pattern Recognition, Clarendon Press, Oxford, (1996).
  • Kil D.H., Shin F.B.,”Pattern Recognition and Prediction with Applications to Signal Characterization”, AIP Press, USA, (1996).
  • Turkoglu, I., “Automatic Target Distance Recognition by Using Continuous Wave Radar Doppler Signals and Artificial Neural Networks”, DAUM, 1, 80-87, Elazig, (2002).
  • Turkoglu, I., “An Intelligent Pattern Recognition for Non-stationary Signals Based on The Time – Frequency Entropies”, PhD Thesis, Firat University Graduate School of Natural and Applied Sciences, Elazig, 40-47, (2002).
  • Percival D. B., Walden A. T., “Spectral analysis for physical applications:Multitaper and conventional univariate techniques”, Cambridge University Press., New York (1993).
  • Lin C. T., Lee C.S.G., “Neural Fuzzy Systems”, Prentice-Hall, (1996).
  • Zurada, M.J., “Introduction to Artificial Neural Systems”, West Publishing Company Inc., New York, (1992).
  • Turkoglu, I., Arslan A., “Power spectrum of the AR model and feature extraction method based on artificial neural network for target classification”, Gazi University, Technical Education Faculty, Journal of Polytechnic , 5(2) , 121- , May, (2002).
  • Kara S., “Doppler cihazı ve autoregresif spektral analiz metoduyla mitral ve triküspit kapaklardaki kan akışının incelenmesi”, Doktora tezi, Erciyes Üniversitesi Fen Bilimleri Enstitüsü, 15-20, (1994).
  • Yüksel M. E., “Ultrasonik Doppler İşaretlerinin Bilgisayar Destekli Analizi”, Y. Lisans tezi, Erciyes Üniversitesi Fen Bilimleri Enstitüsü, 25-43, (1993).
  • Turkoglu I., Arslan A., “Optimisation of the Performance of Neural Network Based Pattern Recognition Classifiers with Distributed Systems”, IEEE Computer Society, International Conference on Parallel and Distributed Systems (ICPADS’2001), ,Kyong Ju, Korea, 379-382 (2001).
  • Kosko, B., Neural Networks and Fuzzy Systems, A Dynamical Systems Approach, Englewood Ciffs., NJ: Prentice Hall, (1991).
  • Jang, J. S. R., Sun, C. T., “Neuro-Fuzzy Modeling and Control”, Proceedings of the IEEE, vol. 83, No. 3, (1995).
  • Jang, J. S. R., May., “ANFIS: Adaptive network-based fuzzy inference systems”, IEEE Trans. Syst., Man. and Cybern., vol. 23, 665-685., (1993).
There are 32 citations in total.

Details

Primary Language English
Journal Section Electrical & Electronics Engineering
Authors

Engin Avcı

İbrahim Türkoğlu This is me

Mustafa Poyraz This is me

Publication Date August 13, 2010
Published in Issue Year 2005 Volume: 18 Issue: 2

Cite

APA Avcı, E., Türkoğlu, İ., & Poyraz, M. (2010). AN INTELLIGENT TARGET RECOGNITION SYSTEM BASED ON PERIODOGRAM FOR PULSED RADAR SYSTEMSPERIODOGRAM FOR PULSED RADAR SYSTEMS. Gazi University Journal of Science, 18(2), 259-272.
AMA Avcı E, Türkoğlu İ, Poyraz M. AN INTELLIGENT TARGET RECOGNITION SYSTEM BASED ON PERIODOGRAM FOR PULSED RADAR SYSTEMSPERIODOGRAM FOR PULSED RADAR SYSTEMS. Gazi University Journal of Science. August 2010;18(2):259-272.
Chicago Avcı, Engin, İbrahim Türkoğlu, and Mustafa Poyraz. “AN INTELLIGENT TARGET RECOGNITION SYSTEM BASED ON PERIODOGRAM FOR PULSED RADAR SYSTEMSPERIODOGRAM FOR PULSED RADAR SYSTEMS”. Gazi University Journal of Science 18, no. 2 (August 2010): 259-72.
EndNote Avcı E, Türkoğlu İ, Poyraz M (August 1, 2010) AN INTELLIGENT TARGET RECOGNITION SYSTEM BASED ON PERIODOGRAM FOR PULSED RADAR SYSTEMSPERIODOGRAM FOR PULSED RADAR SYSTEMS. Gazi University Journal of Science 18 2 259–272.
IEEE E. Avcı, İ. Türkoğlu, and M. Poyraz, “AN INTELLIGENT TARGET RECOGNITION SYSTEM BASED ON PERIODOGRAM FOR PULSED RADAR SYSTEMSPERIODOGRAM FOR PULSED RADAR SYSTEMS”, Gazi University Journal of Science, vol. 18, no. 2, pp. 259–272, 2010.
ISNAD Avcı, Engin et al. “AN INTELLIGENT TARGET RECOGNITION SYSTEM BASED ON PERIODOGRAM FOR PULSED RADAR SYSTEMSPERIODOGRAM FOR PULSED RADAR SYSTEMS”. Gazi University Journal of Science 18/2 (August 2010), 259-272.
JAMA Avcı E, Türkoğlu İ, Poyraz M. AN INTELLIGENT TARGET RECOGNITION SYSTEM BASED ON PERIODOGRAM FOR PULSED RADAR SYSTEMSPERIODOGRAM FOR PULSED RADAR SYSTEMS. Gazi University Journal of Science. 2010;18:259–272.
MLA Avcı, Engin et al. “AN INTELLIGENT TARGET RECOGNITION SYSTEM BASED ON PERIODOGRAM FOR PULSED RADAR SYSTEMSPERIODOGRAM FOR PULSED RADAR SYSTEMS”. Gazi University Journal of Science, vol. 18, no. 2, 2010, pp. 259-72.
Vancouver Avcı E, Türkoğlu İ, Poyraz M. AN INTELLIGENT TARGET RECOGNITION SYSTEM BASED ON PERIODOGRAM FOR PULSED RADAR SYSTEMSPERIODOGRAM FOR PULSED RADAR SYSTEMS. Gazi University Journal of Science. 2010;18(2):259-72.