Browse > Article
http://dx.doi.org/10.5515/KJKIEES.2018.29.2.128

Study on Class Separability Measure for Radar Signals  

Jeong, Seong-Jae (Department of Electrical Engineering, Pohang University of Science and Technology)
Lee, Seung-Jae (Department of Electrical Engineering, Pohang University of Science and Technology)
Kim, Kyung-Tae (Department of Electrical Engineering, Pohang University of Science and Technology)
Publication Information
The Journal of Korean Institute of Electromagnetic Engineering and Science / v.29, no.2, 2018 , pp. 128-137 More about this Journal
Abstract
In this paper, we propose a novel class separability measure for radar signals. To reduce the sensitivity of the relative aspect angle between a target and radar, to evaluate the discriminatory power of radar signals, the proposed method first calculates the correlation coefficients between two radar cross sections (RCSs) or linearly shifts one-dimensional (1D) radar signals (i.e., high-resolution range profiles (HRRPs)), or rotates two 2D radar signals (i.e., inverse synthetic aperture radar (ISAR) images). Then, it uses the maximum correlation coefficient when two radar signals are best aligned. Next, the proposed method obtains new correlation-based discriminant matrices (CDM) using maximum correlation coefficients. Finally, the cumulative distribution function (CDF) in the CDM and the value corresponding to the specific probability in the CDF are obtained, and this value represents the discriminatory power of the radar signal. Experimental results show that the proposed method can accurately measure the target separability.
Keywords
Class Separability Measure; Correlation Coefficient; Classification;
Citations & Related Records
연도 인용수 순위
  • Reference
1 E. Choi, C. Lee, "Feature extraction based on the Bhattacharyya distance," in IGARSS 2000, IEEE 2000 International Geoscience and Remote Sensing Symposium, Honolulu, Jul. 2000, vol. 5, pp. 2146-2148.
2 T. Kailath, "The divergence and Bhattacharyya distance measures in signal selection," IEEE Transactions on Communication Technology, vol. 15, no. 1, pp. 52-60, Feb. 1967.   DOI
3 K. A. Lee, C. H. You, H. Li, T. Kinnunen, and K. C. Sim, "Using discrete probabilities with Bhattacharyya measure for SVM-based speaker verification," IEEE Transactions on Audio, Speech, and Language Processing, vol. 19, no. 4, pp. 861-870, May 2011.   DOI
4 R. Nielsen, R. Nock, "On the Chi square and higher-order Chi distances for approximating f-divergences," IEEE Signal Processing Letters, vol. 21, no. 1, pp. 10-13, Jan. 2014.   DOI
5 P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, "Eigenfaces vs. fisherfaces: Recognition using class specific linear projection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, Jul. 2013.
6 M. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd ed. New York, Academic Press, 1990.
7 S. Theodoridis, K. Koutroumbas, Pattern Recognition, New York, Elsevier Academic Press, 1999.
8 R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. New York, John Wiley & Sons, 2001.
9 V. C. Chen, M. Martorella, Inverse Synthetic Aperture Radar Imaging: Principles, Algorithms and Applications (Electromagnetics and Radar), IET/Scitech, 2014.
10 C. Ozdemir, Inverse Synthetic Aperture Radar Imaging with MATLAB Algorithms, New York, John Wiley & Sons, 2012.
11 Virtual Aircraft Framework for RCS/IR analysis and mitigation( VIRAF). Available: http://www. idscorporation.com.
12 J. Novovicova, P. Pudil, and J. Kittler, "Divergence based feature selection for multimodal class densities," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 2, pp. 218-223, Feb. 1996.   DOI