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An expert target recognition system using a genetic wavelet neural network

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Abstract

In this paper, a target recognition system is presented for target recognition using target echo signals of High Range Resolution (HRR) radars. This paper especially deals with a combination of an adaptive feature extraction and classification using optimum wavelet entropy parameter values. The features are obtained from measured target echo signals using a X-band pulse radar. A genetic wavelet neural network model is developed for target recognition. This model consists of three layers. These layers are genetic algorithm, wavelet analysis and multi-layer perceptron respectively. The genetic algorithm layer is used for selecting the feature extraction method and obtaining the optimum wavelet entropy parameter values. Here, the optimal one of four different feature extraction methods is selected by using a genetic algorithm. The proposed four feature extraction methods are: (i) standard wavelet decomposition, (ii) wavelet decomposition—short-time Fourier transform, (iii) wavelet decomposition—Born-Jordan time-frequency representation, (iv) wavelet decomposition—Choi-Williams time-frequency representation. The wavelet layer is used for optimum feature extraction in the time-frequency domain. It is composed of wavelet decomposition and wavelet entropies. The multi layer perception is used for evaluating the fitness function of the genetic algorithm and for classifying radar targets. The performance of the developed system is evaluated by using noisy radar target echo signals. The test results show that this system is effective in rating real radar target echo signals. The correct classification rate is about 90% for target subjects.

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Abbreviations

TFR:

Time-Frequency Representation

D:

Dimension

FFT:

Fast Fourier Transform

ARTR:

Automatic Radar Target Recognition

ATR:

Automatic Target Recognition

HRR:

High Range Resolution

GWNN:

Genetic Wavelet Neural Network

STFT:

Short Time Fourier Transform

WANFIS:

Wavelet Adaptive Network Fuzzy Inference System

WNN:

Wavelet Neural Network

SNR:

Signal/Noise Ratio

NA:

None Available

References

  1. Ahern J, Delisle GY et al (1989) Radar, vol 1. Lab-Volt Ltd., Canada, pp 4–7

  2. Huynen JR (1991) Physical reality and mathematical process in radar polarimetry. In: Seventh international conference on (IEE) antennas and propagation, ICAP 91, 15–18 Apr 1991, vol 1, pp 257–261

    Google Scholar 

  3. Ahalt SC, Jung T, Krishnamurthy AK (1990) A comparison of radar signal classifiers. In: IEEE international conference on systems engineering, 9–11 Aug 1990, pp 609–612

    Chapter  Google Scholar 

  4. Du L, Liu H, Bao Z (2006) Radar automatic target recognition based on complex high-resolution range profiles. In: International conference on radar CIE’06, 16–19 Oct 2006, pp 1–5

    Chapter  Google Scholar 

  5. Yun Z, Xuelian Y, Minglei C, Xuegang W (2011) Radar target recognition based on multiple features fusion with Dempster-Shafer theory. In: 10th international conference on electronic measurement & instruments (ICEMI), 16–19 Aug 2011, vol 1, pp 243–247

    Google Scholar 

  6. Duc L, Wang P, Liu H, Pan M, Chen F, Bao Z (2011) Bayesian spatiotemporal multitask learning for radar HRRP target recognition. IEEE Trans Signal Process 59(7):3182–3196

    Article  MathSciNet  Google Scholar 

  7. Aldhubaib F, Shuley NV (2010) Radar target recognition based on modified characteristic polarization states. IEEE Trans Aerosp Electron Syst 46(4):1921–1933

    Article  Google Scholar 

  8. Serretta H, Inggs MR (1998) Ship target recognition with the Mellin transform aided by neural networks. In: Symposium on COMSIG’98. Proceedings of the 1998 South African communications and signal processing, 7–8 Sep 1998, pp 203–208

    Chapter  Google Scholar 

  9. Jouny II (2004) Radar backscatter analysis using fractional Fourier transform. In: Antennas and propagation society international symposium, 20–25 June 2004, vol 2. IEEE, New York, pp 2115–2118

    Google Scholar 

  10. Nelson DE, Starzyk JA, Ensley DD (2002) Iterated wavelet transformation and signal discrimination for HRR radar target recognition, IEEE Trans Syst Man Cybern, Part A, Syst Humans 33(1)

  11. Mitchell RA (1997) Hybrid statistical recognition algorithm for aircraft identification. Univ. Dayton Press, Dayton

    Google Scholar 

  12. Antonini M, Barlaud M, Mathieu P, Daubechies I (1992) Image coding using wavelet transform. IEEE Trans Image Process 1(2):205–220

    Article  Google Scholar 

  13. Mallat S, Zhong S (1992) Characterization of signals from multiscale edges. IEEE Trans Pattern Anal Mach Intell 14(7):710–732

    Article  Google Scholar 

  14. Mallat S (1991) Zero crossings of a wavelet transform. IEEE Trans Inf Theory 37(4):1019–1033

    Article  MathSciNet  Google Scholar 

  15. Szu HH (1996) Review of wavelet transforms for pattern recognition. Proc SPIE Wavelet Appl III 2762:2–22

    Google Scholar 

  16. Stirman C, Nachman A (1991) Applications of wavelets to radar data processing. Defense Technical Inform Center, Tech Rep AD-A239 297

  17. Stirman K, Nachman A (1995) Applications of wavelets to automatic target recognition. Defense Technical Inform Center, Tech Rep AD-A294 854

  18. Devaney AJ, Raghavan R, Lev-Ari H, Manolakos E, Kokar M (1997) Automatic target detection and recognition: a wavelet based approach. Northeastern Univ, Defense Technical Inform Center, Tech Rep AD-A329 696

  19. Famili A, Shen W-M, Weber R, Simoudis E (1997) Data preprocessing and intelligent data analysis. Intell Data Anal 1(1)

  20. Misiti M, Nisiti Y, Oppenheim G, Poggi J (1996) Wavelet toolbox user’s guide. MathWorks, Inc, Natick

    Google Scholar 

  21. Strang G, Nguyen T (1996) Wavelets and filter banks. Wellesley, Cambridge

    Google Scholar 

  22. Devaney AJ, Hisconmez B (1994) Wavelet signal processing for radar target identification a scale sequential approach. Proc SPIE Wavelet Appl 2242:389–399

    Google Scholar 

  23. Etemad K, Chellapa R (1998) Separability-based multiscale basis selection and feature extraction for signal and image classification. IEEE Trans Image Process 7(10):1453–1465

    Article  Google Scholar 

  24. Lu J, Algazi VR, Estes RR (1996) Comparative study of wavelet image coders. Opt Eng 35(9):2605–2619

    Article  Google Scholar 

  25. Bishop CM (1996) Neural networks for pattern recognition. Clarendon Press, Oxford

    MATH  Google Scholar 

  26. Akay M (1997) Wavelet applications in medicine. IEEE Spectr 34(5):50–56

    Article  Google Scholar 

  27. Quiroga RQ (1998) Quantitative analysis of EEG signals: time–frequency methods and chaos theory. PhD Thesis, Institute of Physiology, Medical University Lübeck, Lübeck

  28. Devasahayam SR (2000) Signals and systems in biomedical engineering. Kluwer Academic, Dordrecht

    Book  Google Scholar 

  29. Burrus CS, Gopinath RA, Guo H (1998) Introduction to wavelets and wavelet transforms. Prentice Hall, Englewood Cliffs

    Google Scholar 

  30. Keeton PIJ, Schlindwein FS (1997) Application of wavelets in Doppler ultrasound. Sens Rev 17(1):38–45

    Article  Google Scholar 

  31. Zou R, Cupples WA, Yip KP, Holstein-Rathlou NH, Chon KH (2002) Time-varying properties of renal autoregulatory mechanisms. IEEE Trans Biomed Eng 49:10

    Article  Google Scholar 

  32. Boashash B (1992) Time-frequency signal analysis methods and applications. Longman Cheshire, Melbourne

    Google Scholar 

  33. Coifman RR, Wickerhauser MV (1992) Entropy-based algorithms for best basis selection. IEEE Trans Inf Theory 38(2):713–718

    Article  MATH  Google Scholar 

  34. Wavelet toolbox user’s guide for MATLAB 7, 2011

  35. Obitko M (1998) Introduction to Genetic Algorithms. http://cs.felk.cvut.cz/~xobitko/ga/

  36. Haykin S (1994) Neural networks: a comprehensive foundation. Macmillan, New York

    MATH  Google Scholar 

  37. Jakubiak A, Arabas J, Grabczak K et al (1997) Radar clutter classification using Kohonen neural network. In: Radar 97 (Conf Publ No 449). Edinburgh, UK, pp 185–188

    Chapter  Google Scholar 

  38. Tang B, Jiang W, Ke Y (1996) Radar signal classification by projection onto wavelet packet subspaces. In: Proceedings of the CIE international conference of radar, Beijing, China, pp 124–126

    Google Scholar 

  39. Zyweck A, Bogner RE (1994) Radar target recognition using range profiles. In: IEEE international conference on acoustics, speech, and signal processing, ICASSP-94, Adelaide, Australia, vol 2, pp II/373–II/376

    Google Scholar 

  40. Richards MA (2000) Fundamentals of radar signal processing, 3rd edn. McGraw-Hill, New York

    Google Scholar 

  41. Lee W-K, Griffiths HD (2000) A new pulse compression technique generating optimal uniform range sidelobe and reducing integrated sidelobe level. In: The record of the IEEE 2000 international radar conference, pp 441–446

    Google Scholar 

  42. Turkoglu I, Arslan A, Ilkay E (2002) An expert system for diagnosis of the heart valve diseases. Expert Syst Appl 23(3):229–236

    Article  Google Scholar 

  43. Turkoglu I, Arslan A, Ilkay E (2003) An intelligent system for diagnosis of the heart valve diseases with wavelet packet neural networks. Comput Biol Med 33(4):319–331

    Article  Google Scholar 

  44. Avci E, Turkoglu I, Poyraz M (2005) Intelligent target recognition based on wavelet packet neural network. Expert Syst Appl 29(1):175–182

    Article  Google Scholar 

  45. Avci E, Turkoglu I, Poyraz M (2005) Intelligent target recognition based on wavelet adaptive network based fuzzy inference system. In: Lecture notes in computer science, vol 3522. Springer, Berlin, pp 594–601

    Google Scholar 

  46. Mirghasemi S, Yazdi HS, Lotfizad M (2011) A target-based color space for sea target detection. Appl Intell. doi:10.1007/s10489-011-0307-y. Online First™, 29 June 2011

  47. Valova I, Milano G, Bowen K, Gueorguieva N (2011) Bridging the fuzzy, neural and evolutionary paradigms for automatic target recognition. Appl Intell 35(2):211–225

    Article  Google Scholar 

  48. Drake KC, Kim RY (1995) Hierarchical integration of sensor data and contextual information for automatic target recognition. Appl Intell 5(3):269–290

    Article  Google Scholar 

  49. Lehmann J, Bader S, Hitzler P (2010) Extracting reduced logic programs from artificial neural networks. Appl Intell 32(3):249–266

    Article  Google Scholar 

  50. Cholette ME, Liu J, Djurdjanovic D, Marko KA (2011) Monitoring of complex systems of interacting dynamic systems. Appl Intell. doi:10.1007/s10489-011-0313-0. Online First™, 13 August 2011

  51. Altınçay H (2006) On the independence requirement in Dempster-Shafer theory for combining classifiers providing statistical evidence. Appl Intell 25(1):73–90

    Article  Google Scholar 

  52. Tidhar G, Heinze C, Selvestrel M (1998) Flying together: modelling air mission teams. Appl Intell 8(3):195–218

    Article  Google Scholar 

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  1. Department of Software Engineering, Firat University, 23119, Elazig, Turkey

    Engin Avci

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Avci, E. An expert target recognition system using a genetic wavelet neural network. Appl Intell 37, 475–487 (2012). https://doi.org/10.1007/s10489-012-0341-4

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