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

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.

[1]  S. J. Roome Classification of radar signals in modulation domain , 1992 .

[2]  J. Wenli,et al.  Radar signal classification by projection onto wavelet packet subspaces , 1996, Proceedings of International Radar Conference.

[3]  A. Zyweck,et al.  Radar target recognition using range profiles , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[4]  Bart Kosko,et al.  Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence , 1991 .

[5]  Chin-Teng Lin,et al.  Neural fuzzy systems , 1994 .

[6]  A. Walden,et al.  Spectral analysis for physical applications : multitaper and conventional univariate techniques , 1996 .

[7]  Hichem Sahli,et al.  Signal processing and pattern recognition methods for radar AP mine detection and identification , 1998 .

[8]  Z. Swiatnicki,et al.  The artificial intelligence tools utilization in radar signal processing , 1998, 12th International Conference on Microwaves and Radar. MIKON-98. Conference Proceedings (IEEE Cat. No.98EX195).

[9]  Mark A. Richards,et al.  Fundamentals of Radar Signal Processing , 2005 .

[10]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[11]  David H. Kil,et al.  Pattern Recognition and Prediction with Applications to Signal Processing (Aip Series in Modern Acoustics and Signal Processing) , 1998 .

[12]  W. D. Beastall Recognition of radar signals by neural network , 1989 .

[13]  Guangyi Chen,et al.  Applications of wavelet transforms in pattern recognition and de-noising , 1999 .

[14]  J. R. Casar Corredera,et al.  A neural network approach to Doppler-based target classification , 1992 .

[15]  G. P. Noone,et al.  A neural approach to automatic pulse repetition interval modulation recognition , 1999, 1999 Information, Decision and Control. Data and Information Fusion Symposium, Signal Processing and Communications Symposium and Decision and Control Symposium. Proceedings (Cat. No.99EX251).

[16]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[17]  Ahmed H. Tewfik,et al.  Waveform selection in radar target classification , 2000, IEEE Trans. Inf. Theory.

[18]  Zhi-Quan Luo,et al.  The minimum description length criterion applied to emitter number detection and pulse classification , 1998, Ninth IEEE Signal Processing Workshop on Statistical Signal and Array Processing (Cat. No.98TH8381).

[19]  King-Sun Fu,et al.  Hybrid Approaches to Pattern Recognition , 1982 .

[20]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[21]  D. H. Kil,et al.  Pattern recognition and prediction with applications to signal characterization , 1996 .

[22]  Jarosław Arabas,et al.  Radar clutter classification using Kohonen neural network , 1997 .

[23]  Jacek M. Zurada,et al.  Introduction to artificial neural systems , 1992 .

[24]  Ahmet Arslan,et al.  Optimisation of the performance of neural network based pattern recognition classifiers with distributed systems , 2001, Proceedings. Eighth International Conference on Parallel and Distributed Systems. ICPADS 2001.

[25]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.