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  • Research Article
  • Open Access

Target Identification Using Harmonic Wavelet Based ISAR Imaging

  • 1Email author,
  • 1,
  • 1,
  • 1 and
  • 1
EURASIP Journal on Advances in Signal Processing20062006:086053

  • Received: 30 April 2005
  • Accepted: 23 November 2005
  • Published:


A new approach has been proposed to reduce the computations involved in the ISAR imaging, which uses harmonic wavelet-(HW) based time-frequency representation (TFR). Since the HW-based TFR falls into a category of nonparametric time-frequency (T-F) analysis tool, it is computationally efficient compared to parametric T-F analysis tools such as adaptive joint time-frequency transform (AJTFT), adaptive wavelet transform (AWT), and evolutionary AWT (EAWT). Further, the performance of the proposed method of ISAR imaging is compared with the ISAR imaging by other nonparametric T-F analysis tools such as short-time Fourier transform (STFT) and Choi-Williams distribution (CWD). In the ISAR imaging, the use of HW-based TFR provides similar/better results with significant (92%) computational advantage compared to that obtained by CWD. The ISAR images thus obtained are identified using a neural network-based classification scheme with feature set invariant to translation, rotation, and scaling.


  • Fourier
  • Fourier Transform
  • Information Technology
  • Analysis Tool
  • Quantum Information

Authors’ Affiliations

Central Research Laboratory, Bharat Electronics Limited, Bangalore, 560013, India


  1. Chen VC, Qian S: Joint time-frequency transform for radar range-Doppler imaging. IEEE Transactions on Aerospace and Electronic Systems 1998, 34(2):486–499. 10.1109/7.670330View ArticleGoogle Scholar
  2. Chen VC, Ling H: Time-Frequency Transforms for Radar Imaging and Signal Analysis. Artech House, Boston, Mass, USA; 2002.MATHGoogle Scholar
  3. Kim H, Ling H: Wavelet analysis of radar echo from finite-size targets. IEEE Transactions on Antennas and Propagation 1993, 41(2):200–207. 10.1109/8.214611View ArticleGoogle Scholar
  4. Kim K-T, Choi I-S, Kim H-T: Efficient radar target classification using adaptive joint time-frequency processing. IEEE Transactions on Antennas and Propagation 2000, 48(12):1789–1801. 10.1109/8.901267View ArticleGoogle Scholar
  5. Chen VC: Reconstruction of inverse synthetic aperture radar image using adaptive time-frequency wavelet transform. Wavelet Applications II, April 1995, Orlando, Fla, USA, Proceedings of SPIE 2491: 373–386.View ArticleGoogle Scholar
  6. Choi I-S, Cho B-L, Kim H-T: ISAR motion compensation using evolutionary adaptive wavelet transform. IEE Proceedings of Radar, Sonar and Navigation 2003, 150(4):229–233. 10.1049/ip-rsn:20030639View ArticleGoogle Scholar
  7. Cohen L: Time-Frequency Analysis. Prentice-Hall, Englewood Cliffs, NJ, USA; 1995.Google Scholar
  8. Cohen L: Time-frequency distributions—a review. Proceedings of the IEEE 1989, 77(7):941–981. 10.1109/5.30749View ArticleGoogle Scholar
  9. Newland DE: Practical signal analysis: do wavelets make any difference? Proceedings of Design Engineering Technical Conferences (DETC '97), September 1997, Sacramento, Calif, USAGoogle Scholar
  10. Newland DE: Random Vibrations, Spectral and Wavelets Analysis. 3rd edition. Longman, Singapore; 1993.Google Scholar
  11. Newland DE: Wavelet analysis of vibration, part I: theory. Transactions of the ASME: Journal of Vibration and Acoustics 1994, 116(4):409–416. 10.1115/1.2930443Google Scholar
  12. Newland DE: Time-frequency and time-scale signal analysis by harmonic wavelets. Proceedings of the 1st European Conference on Signal Analysis and Prediction, June 1997, Prague, Czech Republic 53–59.Google Scholar
  13. Oja E: Neural networks, principal components, and subspaces. International Journal of Neural Systems 1989, 1(1):61–68. 10.1142/S0129065789000475View ArticleGoogle Scholar
  14. Comon P: Independent component analysis, a new concept? Signal Processing 1994, 36(3):287–314. 10.1016/0165-1684(94)90029-9View ArticleGoogle Scholar
  15. Al-Ani A, Deriche M: A hybrid information maximisation (HIM) algorithm for optimal feature selection from multi-channel data. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '00), June 2000, Istanbul, Turkey 6: 3470–3473.Google Scholar
  16. Devijver PA, Kittler J: Pattern Recognition: A Statistical Approach. Prentice-Hall, London, UK; 1982.MATHGoogle Scholar
  17. Pal SK: Fuzzy set theoretic measures for automatic feature evaluation: II. Information Sciences 1992, 64(1–2):165–179. 10.1016/0020-0255(92)90118-RView ArticleGoogle Scholar
  18. Battiti R: Using mutual information for selecting features in supervised neural net learning. IEEE Transactions on Neural Networks 1994, 5(4):537–550. 10.1109/72.298224View ArticleGoogle Scholar
  19. Belue LM, Bauer KW Jr.: Determining input features for multilayer perceptrons. Neurocomputing 1995, 7(2):111–121. 10.1016/0925-2312(94)E0053-TView ArticleGoogle Scholar
  20. Ruck DW, Rogers SK, Kabrisky M: Feature selection using a multilayer perceptron. Journal of Neural Network Computing 1990, 2(2):40–48.Google Scholar
  21. Wunsch P, Laine AF: Wavelet descriptors for multiresolution recognition of handprinted characters. Pattern Recognition 1995, 28(8):1237–1249. 10.1016/0031-3203(95)00001-GView ArticleGoogle Scholar
  22. Daubechies I: Ten Lectures on Wavelets, CBMS-NSF Regional Conference Series in Applied Mathematics. Volume 61. 2nd edition. SIAM, Philadelphia, Pa, USA; 1992.Google Scholar
  23. Gonzalez RC, Woods RE: Digital Image Processing. Addison Wesley, Boston, Mass, USA; 1993.Google Scholar
  24. Yokoyama Y, Miyamoto Y, Ohta M: Very low bit rate video coding using arbitrarily shaped region-based motion compensation. IEEE Transactions on Circuits and Systems for Video Technology 1995, 5(6):500–507. 10.1109/76.475892View ArticleGoogle Scholar
  25. Granulund GH: Fourier processing for handwritten character recognition. IEEE Transactions on Computers 1992, 21: 195–201.Google Scholar


© Kumar et al. 2006