TELECOMMUNICATIONS AND RADIO ENGINEERING - 2010 Vol. 69,
No 8 		 

 

 

 

Reduction of Aspect Dependent Speckle Fluctuations in High-Resolution Radar Range Profiles

J T. Astola & K O. Egiazarian
Tampere University of Technology, Signal Processing Laboratory,
P. O. Box 553, FIN-33101, Tampere, Finland

P A. Molchanov & A.V. Totsky
National Aerospace University,
17, Chkalova Str., 61070, Kharkiv, Ukraine
Address all correspondence to A.V. Totsky E-mail: totskiy@xai.edu.

V M. Orlenko
Kharkov Air Forces University,
77/79, Sumskaya Str., 61023, Kharkiv, Ukraine

Abstract
This paper deals with the analysis of the variability of high-resolution radar range profiles caused by the changes of aerial target aspect angle regarding to the radar line of sight. An approach based on radar range profiles reconstruction from the averaging of sets of short-time bispectrum estimates is developed and investigated. Results of computer simulations obtained for aerial targets of different types demonstrate that the sensibility of range profile estimates with respect to the target aspect angle changes decreases when the proposed bispectrum-based estimation is used.

KEY WORDS: high-resolution range profile, automatic target recognition, speckle, bispectral estimation, aircraft



References
  1. Shirman, Ya.D., Gorshkov, S.A., Leshenko, S.P., Orlenko, V.M., Sedyshev, S.Y., and Sukharevskiy, O.I., (2002), Computer simulation of aerial targets, radar scattering, recognition, detection, and tracking, Boston-London, Artech House.
  2. Kisienski, A.A., Lin, Y.-T., and White, L.J., (1975), Low-frequency approach to target identification, Proceedings of the IEEE, 63:1651-1659.
  3. Van Der Heiden, R. and Groen, F.C.A., (1997), The Box-Cox metric for nearest neighbour classification improvement, Pattern Recognition, 30(2):273-279.
  4. Zwart, J.P., van Der Heiden, R., Gelsema, S., and Groen, F., (2003), Fast translation invariant classification of HRR range profiles in a zero phase representation, IEEE Proceedings Radar, Sonar and Navigation, 150(6):411-418.
  5. Webb, A.R., (2000), Gamma mixture models for target recognition, Pattern Recognition, 33(12):2045-2054.
  6. Roth, M.W., (1990), Survey of neural network technology for automatic target recognition, IEEE Transactions on Neural Networks, 1(1):28-43.
  7. Rogers, S.K. et al., (2000), Neural networks for automatic target recognition, Neural Networks, 8(7/8):1153-1184.
  8. Chan, L.A., Nasrabadi, N.M., and Mirelli, V., (1996), Multi-stage target recognition using modular vector quantizers and multilayer perceptrons, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 114-119.
  9. Eom, K.B., (1999), Time-varying autoregressive modeling of HRR radar signatures, IEEE Transactions on Aerospace and Electronic Systems, 35(3):974-988.
  10. Rizki, M.M., Zmuda, M.A., and Tamburino, L.A., (2002), Evolving pattern recognition systems, IEEE Transactions on Evolutionary Computation, 6(6):594-609.
  11. Mitchell, R.A. and Westerkamp, J.J., (1999), Robust statistical feature based aircraft identification, IEEE Transactions on Aerospace and Electronic Systems, 35(3):1077-1094.
  12. Nelson, D.E., Starzyk, J.A., and Ensley, D.D., (2003), Iterated wavelet transformation and signal discrimination for HRR radar target recognition, IEEE Transactions on Systems, Man and Cybernetics, 33(1):52-57.
  13. Zhoum, D., Liu, G., and Wang, J., (2000), Spatio-temporal target identification method of high-range resolution radar, Pattern Recognition, 33(1):1-7.
  14. Spooner, C.M., (2001), Application of local discriminant bases to HRR-based ATR, Conference on Signals, Systems and Computers, Conference Record of the Thirty-Fifth Asilomar, 2:1067-1073.
  15. Gil Pita, R., Jarabo Amores, P., Rosa Zurera, M., and López Ferreras, F., (2002), Improving neural classifiers for ATR using a kernel method for generating synthetic training sets, Proceedings of the 2002 IEEE Signal Processing Society Workshop “Neural Networks for Signal Processing”, pp. 425-434.
  16. Abu-Mostafa, Y.S., (1995), Hints, Neural Computation, 7:699-671.
  17. Niyogi, P., Girosi, F., and Poggio, T., (1998), Incorporating prior information in machine learning by creating virtual examples, Proceedings of the IEEE, 86(11):2196-2209.
  18. Smith, C.R. and Goggans, P.M., (1993), Radar Target Identification, IEEE Antennas and Propagation Magazine, 35(2):27-37.
  19. Nikias, C.L. and Raghuveer, M.R., (1987), Bispectral estimation: A digital signal processing framework, Proc. IEEE, 75(7):869-891.
  20. Xian-Da Zhang, Yu Shi, and Zheng Bao, (2001), A new feature vector using selected bispectra for signal classification with application in radar target recognition, IEEE Trans. Signal Processing, 49(9):1875-1885.
  21. Totsky, A.V., Kurbatov, I.V., Lukin, V.V., Egiazarian, K.O., and Astola, J.T., (2003), Combined bispectrum-filtering techniques for radar output signal reconstruction in ATR applications, Proceedings of International Conference Automatic Target Recognition XIII; SPIE 5094:301-312.
  22. Lan Du, Hongway Liu, Zheng Bao, and Mengdao Xing, (2005), Radar HRRP target recognition based on higher order spectra, IEEE Trans. Signal Processing, 53(7):2359-2368.
  23. Sundaramoorthy, G., Raghuveer, M.R., and Dianat, S.A., (1990), Bispectral reconstruction of signals in noise: Amplitude reconstruction issues, IEEE Transact. Acoustics, Speech, Signal Processing, 38(7):1297-1306.
  24. Bartelt, H., Lohmann, A.W., and Wirnitzer, B., (1984), Phase and amplitude recovery from bispectra, Applied Optics, 23:3121-3129.


pages 687-698

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