Radar Target Recognition by Machine Learning of K-Nearest Neighbors Regression on Angular Diversity RCS
Keywords:
Machine learning, radar cross section, radar target recognitionAbstract
In this paper, the radar target recognition is given by machine learning of K-NN (K-nearest neighbors) regression on angular diversity RCS (radar cross section). The bistatic RCS of a target at a fixed elevation angle and different azimuth angles are collected to constitute an angular diversity RCS vector. Such angular diversity RCS vectors are chosen as features to identify the target. Different RCS vectors are collected and processed by the K-NN regression. The machine learning belongs to the scope of artificial intelligence, which has attracted the attention of researchers all over the world. In this study, the K-NN rule is extended to achieve regression and is then applied to radar target recognition. With the use of K-NN regression, the radar target recognition is very simple, efficient, and accurate. Numerical simulation results show that our target recognition scheme is not only accurate, but also has good ability to tolerate random fluctuations.
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References
N. H. Farhat, “Microwave diversity imaging and automated target identification based on models of neural networks,” Proc. IEEE, vol. 77, no. 5, pp. 670-681, 1989.
K. C. Lee, “Polarization Effects on Bistatic Microwave Imaging of Perfectly Conducting Cylinders,” Master Thesis, National Taiwan University, Taipei, Taiwan, 1991.
K. C. Lee, “Inverse scattering of a conducting cylinder in free space by modified fireworks algorithm,” Prog. Electromagn. Res. M, vol. 59, pp. 135-146, 2017.
K. C. Lee, J. S. Ou, and C. W. Huang, “Angulardiversity radar recognition of ships by transformation based approaches --- including noise effects,” Prog. Electromagn. Res., vol. 72, pp. 145- 158, 2007.
S. Theodoridis and K. Koutroumbas, Pattern Recognition. 2nd ed., Academic Press, Boston, 2003.
G. T. Ruck, D. E. Barrick, W. D. Stuart, and C. K. Krichbaum, Radar Cross Section Handbook. vol. 1, Plenum, New York, 1970.
S. Suthaharan, Machine Learning Models and Algorithms for Big Data Classification. Springer, New York, 2016.
S. Caorsi, D. Anguita, E. Bermani, A. Boni, M. Donelli, and A. Massa, “A comparative study of NN and SVM-based electromagnetic inverse scattering approaches to on-line detection of buried objects,” Appl. Comput. Electromagn. Soc. J., vol. 18, no. 2, pp. 1-11, 2003.
A. Kabiri, N. Sarshar, and K. Barkeshli, “Application of neural networks in the estimation of two-dimensional target orientation,” Appl. Comput. Electromagn. Soc. J., vol. 18, no. 2, pp. 57-63, 2003.
D. Shi and Y. Gao, “Electromagnetic radiation source identification based on spatial characteristics by using support vector machines,” Appl. Comput. Electromagn. Soc. J., vol. 32, no. 2, pp. 120-127, 2017.
T. M. Cover and P. E. Hart, “Nearest neighbor pattern classification,” IEEE Trans. Inf. Theory, vol. IT-13, no. 1, pp. 21-27, 1967.
P. E. Hart, “The condensed nearest neighbor rule,” IEEE Trans. Inf. Theory, vol. IT-14, no. 3, pp. 515- 516, 1968.
P. Hall, B. U. Park, and R. J. Samworth, “Choice of neighbor order in nearest neighbor classification,” Ann. Stat., vol. 36, no. 5, 2135-2152, 2008.
I. Mesecan and I. Bucak, “Searching the effects of image scaling for underground object detection using K-means and K-NN,” The 8th European Modelling Symposium (EMS), Pisa, Italy, pp. 180- 184, Oct. 2014.
N. S. Altman, “An introduction to kernel and nearest-neighbor nonparametric regression,” Am. Stat., vol. 46, no. 3, pp. 175-185, 1992.
C. T. Tai, Dyadic Green's Functions in Electromagnetic Theory. International Textbook Company, San Francisco, 1971.
R. F. Harrington, Field Computation by Moment Methods. Macmillan, New York, 1968.
K. C. Lee, J. S. Ou, and M. C. Fang, “Application of SVD noise-reduction technique to PCA based radar target recognition,” Prog. Electromagn. Res., vol. 81, pp. 447-459, 2008.
T. K. Moon, and W. C. Stirling, Mathematical Methods and Algorithms for Signal Processing. Prentice Hall, New Jersey, 2000.
