Progress In Electromagnetics Research
ISSN: 1070-4698, E-ISSN: 1559-8985
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By K.-C. Lee, J.-S. Ou, and C.-W. Huang

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In this paper, the angular-diversity radar recognition of ships is given by transformation based approaches with noise effects taken into consideration. The ships and sea roughness are considered by simplified models in the simulation. The goal is to identify the similarity between the unknown target ship and known ships. Initially, the angular-diversity radar cross sections (RCS) from a ship are collected to constitute RCS vectors (usually largedimensional). These RCS vectors are projected into the eigenspace (usually small-dimensional) and radar recognition is then performed on the eigenspace. Numerical examples show that high recognition rate can be obtained by the proposed schemes. The radar recognition of ships in this study is straightforward and efficient. Therefore, it can be applied to many other radar applications.

Citation: (See works that cites this article)
K.-C. Lee, J.-S. Ou, and C.-W. Huang, "Angular-Diversity Radar Recognition of Ships by Transformation Based Approaches --- Including Noise Effects," Progress In Electromagnetics Research, Vol. 72, 145-158, 2007.

1. Hajduch, G., J. M. Le Caillec, and R. Garello, "Airborne high-resolution ISAR imaging of ship targets at sea," IEEE Transactions on Aerospace and Electronic Systems, Vol. 40, No. 1, 378-384, 2004.

2. Tello, M., C. Lopez-Martinez, and J. J. Mallorqui, "A novel algorithm for ship detection in SAR imagery based on the wavelet transform," IEEE Geoscience and Remote Sensing Letters, Vol. 2, No. 2, 201-205, 2005.

3. Farhat, N. H., Microwave diversity imaging and automated target identification based on models of neural networks, IEEE Proceedings, Vol. 77, No. 5, 670-681, 1989.

4. Lee, K. C., Polarization Effects on Bistatic Microwave Imaging of Perfectly Conducting Cylinders, Master thesis, National Taiwan University, Taipei, Taiwan, 1991.

5. Moon, T. K. and W. C. Stirling, Mathematical Methods and Algorithms for Signal Processing, Prentice Hall, 2000.

6. Duda, R. O., P. E. Hart, and D. G. Stork, Pattern Classification, 2nd edition, John Wiley & Sons Inc., 2001.

7. Theodoridis, S. and K. Koutroumbas, Pattern Recognition, 2nd edition, Academic Press, Boston, 2003.

8. Ruck, G. T., D. E. Barrick, W. D. Stuart, and C. K. Krichbaum, Radar Cross Section Handbook, Vol. 1, Vol. 1, Plenum, New York, 1970.

9. Bermani, E., A. Boni, A. Kerhet, and A. Massa, "Kernels evaluation of svm-based estimators for inverse scattering problems," Progress In Electromagnetics Research, Vol. 53, 167-188, 2005.

10. Azaro, R., A. Casagranda, D. Franceschini, and A. Massa, "An innovative fuzzy-logic-based strategy for an effective exploitation of noisy inverse scattering data," Progress In Electromagnetics Research, Vol. 54, 283-302, 2005.

11. Chiang, C. T. and B. K. Chung, "High resolution 3-D imaging," Journal ofEle ctromagnetic Waves and Applications, Vol. 19, No. 9, 1267-1281, 2005.

12. Guo, B., Y. Wang, J. Li, P. Stoica, and R. Wu, "Microwave imaging via adaptive beamforming methods for breast cancer detection," Journal ofEle ctromagnetic Waves and Applications, Vol. 20, No. 1, 53-63, 2006.

13. Semenov, S. Y., V. G. Posukh, A. E. Bulyshev, T. C. Williams, Y. E. Sizov, P. N. Repin, A. Souvorov, and A. Nazarov, "Microwave tomographic imaging of the heart in intact swine," Journal ofEle ctromagnetic Waves and Applications, Vol. 20, No. 7, 873-890, 2006.

14. Chen, X., D. Liang, and K. Huang, "Microwave imaging 3- D buried objects using parallel genetic algorithm combined with FDTD technique," Journal ofEle ctromagnetic Waves and Applications, Vol. 20, No. 13, 1761-1774, 2006.

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