Dual Space SVDD Based Radar Target Recognition from HRRP

XIAO Huaitie; FENG Guoyu; ZHU Yongfeng; HUANG Mengjun

Article Contents

Article Navigation > Chinese Journal of Electronics > 2014 > 23(2): 419-425

XIAO Huaitie, FENG Guoyu, ZHU Yongfeng, et al., “Dual Space SVDD Based Radar Target Recognition from HRRP,” Chinese Journal of Electronics, vol. 23, no. 2, pp. 419-425, 2014,

Citation:

XIAO Huaitie, FENG Guoyu, ZHU Yongfeng, et al., “Dual Space SVDD Based Radar Target Recognition from HRRP,” Chinese Journal of Electronics, vol. 23, no. 2, pp. 419-425, 2014,

XIAO Huaitie, FENG Guoyu, ZHU Yongfeng, et al., “Dual Space SVDD Based Radar Target Recognition from HRRP,” Chinese Journal of Electronics, vol. 23, no. 2, pp. 419-425, 2014,

Citation:

XIAO Huaitie, FENG Guoyu, ZHU Yongfeng, et al., “Dual Space SVDD Based Radar Target Recognition from HRRP,” Chinese Journal of Electronics, vol. 23, no. 2, pp. 419-425, 2014,

PDF( 274 KB)

Dual Space SVDD Based Radar Target Recognition from HRRP

Science and Technology on Automatic Target Recognition Laboratory, National University of Defense Technology, Changsha 410073, China

Funds: This work is supported by the National Natural Science Foundation of China (No.60572138, No.61372159).

Received Date: 2012-06-01
Rev Recd Date: 2013-04-01
Publish Date: 2014-04-05

Abstract

Abstract

In radar target recognition based on kernel method, Support vector data description (SVDD) has been applied to High resolution range profiles (HRRPs) recognition. In this paper, first, three distribution models, i.e., Membership model, Cloud model, Gaussian mixture model, are developed to describe distribution characteristics of HRRPs in extended space. Secondly, test HRRPs in multi-target hypersphere spaces are classified, in accordance with their multi-space distributing characteristics, into two types, i.e., shrink sample and slack sample. Determination of the property of a test sample is achieved by using the minimum relative distance for shrink samples and three distribution models for slack samples, respectively. Thus three HRRP recognition methods based on dual space SVDD are formed. The extensive experiment results for HRRPs of four planes show that the proposed methods have better recognition performance than the recognition method based on single space SVDD.
- High resolution range profile,
- Support vector data description,
- Membership model,
- Cloud model,
- Gaussian mixture model

FullText(HTML)

References(16)

References

F.Z. Dai, H.W. Liu and S.J. Wu, "Subband implementation for wideband radar clutter suppression and target HRRP enhancement", Chinese Journal of Electronics, Vol.19, No.2, pp.381-385, 2010.

L. Yuan, H.W. Liu, Z. Bao, "Automatic target recognition of radar HRRP based on central moments features", Acta Electronics Sinica, Vol.32, No.12, pp.2078-2081, 2004. (in Chinese)

L. Guo, H.T. Xiao, Q. Fu, "Radar target fuzzy recognition method based on KPCA and SVDD", Signal Processing, Vol.25, No.1, pp.63-68, 2009. (in Chinese)

D.M.J. Tax, R.P.W. Duin, "Support vector data description", Machine Learning, Vol.54, No.1, pp.45-46, 2004.

Y.H. Liu, S.H. Lin, Y.L. Hsueh, "Automatic target defect identification for TFT-LCD array process inspection using kernel FCM-based fuzzy SVDD ensemble", Expert Systems with Applications, Vol.36, No.2, pp.1978-1998, 2009.

C. Lai, D.M.J. Tax, R.P.W. Duin, "A study on combing image representations for image classification and retrieval", Pattern Recognition, Vol.18, No.5, pp.867-890, 2004.

M. Jordi, B. Lorenzo, C.A. Gustavo, "Support vector domain description approach to supervised classification of remote sensing images", IEEE Transactions on Geoscience and Remote Sensing, Vol.45, No.8, pp.2683-2692, 2007.

X. Yu, "Research on algorithms of HRRP target recognition based on kernel method", M.S. Thesis, National University of Defense Technology, China, 2008. (in Chinese)

R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification, 2nd ed, New York, Wiley, 2001.

D.Y. Li, J.W. Han, X.M. Shi, "Knowledge representation and discovery based on linguistic atoms", Knowledge-Based Systems, No.10, pp.421-440, 1998.

L.J. Shi, Y.X. Wen, X.G. Xie, "Classifier based on cloud model and its application", International Conference on Computational Intelligence and Software Engineering, Wuhan, China, pp.1-4, 2009.

D.Y. Li, C.Y. Liu, "Study on the universality of the normal cloud model", Chinese Engineering Science, Vol.6, No.8, pp.28-34, 2004. (in Chinese)

L. Du, H.W. Liu, Z. Bao, "Radar HRRP statistical recognition parametric model and model selection", IEEE Transactions on Signal Processing, Vol.56, No.5, pp.1931-1944, 2008.

L. Du, H.W. Liu, Z. Bao, "A two-distribution compounded statistical model for radar HRRP target recognition", IEEE Transactions on Signal Processing, Vol.54, No.6, pp.2226-2238, 2006.

D. Rubin, D. Thayer, "EM algorithm for ML factor analysis", Psychometrika, Vol.47, No.1, pp.69-76, 1982.

M.M.S. Lee, S.S. Keerthi, C.J. Ong, "An efficient method for computing leave-one-out error in support vector machines with gaussian kernels", IEEE Transactions on Neural Networks, Vol.15, No.3, pp.750-757, 2004.

Relative Articles

Supplements(0)

Cited By

Proportional views