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2014-09-23
An Analysis of Near-Field Scattering Characteristics of Rough Target: from the Perspective of Bidirectional Reflectance Distribution Function Based on LS-SVM
By
Progress In Electromagnetics Research M, Vol. 39, 1-9, 2014
Abstract
The near-field scattering characteristics of rough target are analyzed by using a revised bidirectional reflectance distribution function (BRDF) of a rough surface based on least squares support vector machine (LS-SVM). The revised BRDF is more reliable in a larger range of incident angles and scattering angles that beyond the scope of experimental measurements. The basic principle of LS-SVM and the modeling process are firstly introduced in detail. Then the comparison among LS-SVM, the back propagation neural network (BPNN) and the measured data is carried out.The results show that the LS-SVM model has better integrative performance, stronger generalization ability and higher precision. On this basis, the calculation of the near-field radar cross section (RCS) of a complex target is safely performed and analyzed. The method proposed is helpful to better investigate the near-field scattering characteristics of rough target.
Citation
Ning Li Min Zhang Ding Nie Wang-Qiang Jiang , "An Analysis of Near-Field Scattering Characteristics of Rough Target: from the Perspective of Bidirectional Reflectance Distribution Function Based on LS-SVM," Progress In Electromagnetics Research M, Vol. 39, 1-9, 2014.
doi:10.2528/PIERM14050801
http://www.jpier.org/PIERM/pier.php?paper=14050801
References

1. Gibbs, D. P., C. L. Betty, A. K. Fung, and A. J. Blanchard, "Automated measurement of polarized bidirectional reflectance," Remote Sensing of Environment, Vol. 43, 97-114, 1993.
doi:10.1016/0034-4257(93)90067-8

2. Li, H. K., N. Pinel, and C. Bourlier, "A monostatic illumination function with surface reflections from one-dimensional rough surfaces," Waves in Random and Complex Media, Vol. 21, 105-134, 2011.
doi:10.1080/17455030.2010.524263

3. Ulaby, F. T., R. K. Moore, and A. K. Fung, Microwave Remote Sensing, Addison-Wesley, New York, 1982.

4. Arai, K., "Method for estimation of grow index of tealeaves based on Bi-directional reflectance distribution function: BRDF measurements with ground based network cameras," International Journal of Applied Sciences, Vol. 2, 52-62, 2011.

5. Jordan, D. L., "Experimental measurements of optical backscattering from surfaces of roughness comparable to the wavelength and their application to radar sea scattering," Waves in Random and Complex Media, Vol. 5, 41-54, 1995.
doi:10.1088/0959-7174/5/1/006

6. Cook, R. L. and K. E. Torrance, "A reflectance model for computer graphics," Computer Graphics, Vol. 15, 307-316, 1981.
doi:10.1145/965161.806819

7. Phong, B. T., "Illumination for computer generated pictures," Communications of the ACM, Vol. 18, 311-317, 1975.
doi:10.1145/360825.360839

8. Ward, G. J., "Measuring and modeling anisotropic reflection," Computer Graphics, Vol. 26, 265-272, 1992.
doi:10.1145/142920.134078

9. Oren, M. and S. K. Nayar, "Generalization of the Lambertian model and implications for machine vision," International Journal Computer Vision, Vol. 14, 227-251, 1995.
doi:10.1007/BF01679684

10. Li, W., J. F. Chen, and T. Wang, "Prediction of the plasma distribution using an artificial neural network," Chinese Physics B, Vol. 18, 2441-2444, 2009.
doi:10.1088/1674-1056/18/6/053

11. Vapnik, V., E. Levin, and Y. Le Cun, "Meaning the VC-dimension of a learning machine," Neural Computation, Vol. 6, 851-876, 1994.
doi:10.1162/neco.1994.6.5.851

12. Balabin, R. M. and E. I. Lomakina, "Support vector machine regression (SVR/LS-SVM)-an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared," Analyst, Vol. 136, 1703-1712, 2011.
doi:10.1039/c0an00387e

13. Wang, H. F. and D. J. Hu, "Comparison of SVM and LS-SVM for regression," International Conference on Neural Networks and Brain, Vol. 1, 279-283, 2005.

14. Suykens, J. A. K. and J. Vandewaiie, "Least squares support vector machine classifiers," Neural Processing Letter, Vol. 9, 293-300, 1999.
doi:10.1023/A:1018628609742

15. Suykens, J. A. K., T. V. Gestel, J. D. Brabanter, B. D. Moor, and J. Vandewalle, Least Squares Support Vector Machines, World Scientific Publishers, Singapore, 2002.

16. Adankon, M. M., M. Cheriet, and A. Biem, "Semisupervised learning using Bayesian interpretation: Application to LS-SVM," IEEE Transactions on Neural Networks, Vol. 22, 513-524, 2011.
doi:10.1109/TNN.2011.2105888

17. Gestel, V., et al., "Financial time series prediction using least squares support vector machines within the evidence framework," IEEE Transactions on Neural Networks, Vol. 12, 809-821, 2001.
doi:10.1109/72.935093

18. Scholkopf, B. and S. Mika, "Input space vs. feature space in Kernel based methods," IEEE Trans. on Neural Networks, Vol. 10, 1000-1017, 1999.
doi:10.1109/72.788641

19. Fletcher, R., Practical Methods of Optimization, John Wiley and Sons, Chichester and New York, 1987.

20. Browne, M. W., "Cross-validation methods," Journal of Mathematical Psychology, Vol. 44, 108-132, 2000.
doi:10.1006/jmps.1999.1279

21. Guo, H., H. P. Liu, and L. Wang, "Method for selecting parameters of least squares support vector machines and application," Journal of System Simulation, Vol. 18, 2033-2036, 2006.

22. Hecht-Nielsen, R., "Theory of the backpropagation neural network," International Joint Conference on IEEE, 93-605, 1989.

23. Tomiyasu, K., "Relationship between and measurement of differential scattering coefficient and bidirectional reflectance distribution function (BRDF)," IEEE Transactions on Geoscience and Remote Sensing, Vol. 26, 660-665, 1988.
doi:10.1109/36.7692

24. Andrews, L. C., M. A. Al-Habash, C. Y. Hopen, and R. L. Phillips, "Theory of optical scintillation: Gaussian-beam wave model," Waves in Random and Complex Media, Vol. 11, 271-291, 2001.