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Target Identification Using Harmonic Wavelet Based ISAR Imaging

EURASIP Journal on Advances in Signal Processing volume 2006, Article number: 086053 (2006) Cite this article

Abstract

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.

References

  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. 10.1109/7.670330

    Article  Google Scholar 

  2. Chen VC, Ling H: Time-Frequency Transforms for Radar Imaging and Signal Analysis. Artech House, Boston, Mass, USA; 2002.

    MATH  Google 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. 10.1109/8.214611

    Article  Google 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. 10.1109/8.901267

    Article  Google 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.

    Article  Google 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:20030639

    Article  Google 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. 10.1109/5.30749

    Article  Google Scholar 

  9. Newland DE: Practical signal analysis: do wavelets make any difference? Proceedings of Design Engineering Technical Conferences (DETC '97), September 1997, Sacramento, Calif, USA

    Google 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. 10.1115/1.2930443

    Google 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/S0129065789000475

    Article  Google Scholar 

  14. Comon P: Independent component analysis, a new concept? Signal Processing 1994, 36(3):287–314. 10.1016/0165-1684(94)90029-9

    Article  Google 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.

    MATH  Google 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-R

    Article  Google 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. 10.1109/72.298224

    Article  Google 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-T

    Article  Google 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-G

    Article  Google 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. 10.1109/76.475892

    Article  Google Scholar 

  25. Granulund GH: Fourier processing for handwritten character recognition. IEEE Transactions on Computers 1992, 21: 195–201.

    Google Scholar 

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Authors and Affiliations

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

    B. K. Shreyamsha Kumar, B. Prabhakar, K. Suryanarayana, V. Thilagavathi & R. Rajagopal

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  1. B. K. Shreyamsha Kumar
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  2. B. Prabhakar
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  3. K. Suryanarayana
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  4. V. Thilagavathi
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  5. R. Rajagopal
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Correspondence to B. K. Shreyamsha Kumar.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Shreyamsha Kumar, B.K., Prabhakar, B., Suryanarayana, K. et al. Target Identification Using Harmonic Wavelet Based ISAR Imaging. EURASIP J. Adv. Signal Process. 2006, 086053 (2006). https://doi.org/10.1155/ASP/2006/86053

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