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An Efficient Kernel Optimization Method for Radar High-Resolution Range Profile Recognition
EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 049597 (2007)
Abstract
A kernel optimization method based on fusion kernel for high-resolution range profile (HRRP) is proposed in this paper. Based on the fusion of
-norm and
-norm Gaussian kernels, our method combines the different characteristics of them so that not only is the kernel function optimized but also the speckle fluctuations of HRRP are restrained. Then the proposed method is employed to optimize the kernel of kernel principle component analysis (KPCA) and the classification performance of extracted features is evaluated via support vector machines (SVMs) classifier. Finally, experimental results on the benchmark and radar-measured data sets are compared and analyzed to demonstrate the efficiency of our method.
References
Chen B, Liu H, Bao Z: PCA and kernel PCA for radar high range resolution profiles recognition. Proceedings of IEEE International Radar Conference, May 2005, Arlington, Va, USA 528–533.
Chen B, Liu H, Bao Z: An efficient kernel optimization method for high range resolution profile recognition. Proceedings of IEEE International Radar Conference, October 2006, Shanghai, China 1440–1443.
Du L, Liu H, Bao Z, Xing M: Radar HRRP target recognition based on higher order spectra. IEEE Transactions on Signal Processing 2005,53(7):2359-2368.
Du L, Liu H, Bao Z, Zhang J: A two-distribution compounded statistical model for radar HRRP target recognition. IEEE Transactions on Signal Processing 2006,54(6):2226-2238.
Xiong H, Swamy MNS, Ahmad MO: Optimizing the kernel in the empirical feature space. IEEE Transactions on Neural Networks 2005,16(2):460-474. 10.1109/TNN.2004.841784
Amari S, Wu S: Improving support vector machine classifiers by modifying kernel functions. Neural Networks 1999,12(6):783-789. 10.1016/S0893-6080(99)00032-5
Vapnik V: The Nature of Statistical Learning Theory. Springer, New York, NY, USA; 1995.
Bao Z, Xing M, Wang T: Radar Imaging Technique. Publishing House of Electronics Industry, Beijing, China; 2005.
Schölkopf B, Mika S, Burges CJC, et al.: Input space versus feature space in kernel-based methods. IEEE Transactions on Neural Networks 1999,10(5):1000-1017. 10.1109/72.788641
Shawe-Taylor J, Cristianini N: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge, UK; 2004.
Ripley BD: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge, UK; 1996.
Blake C, Keogh E, Merz CJ: UCI repository of machine learning databases. Department of Information and Computer Science, University of California, Irvine, Calif, USA; 1998.https://doi.org/www.ics.uci.edu/~mlearn/MLRepository.html
Liu H, Bao Z: Radar HRR profiles recognition based on SVM with power-transformed-correlation kernel. Proceedings of International Symposium on Neural Networks (ISNN '04), August 2004, Dalian, China, Lecture Notes in Computer Science 3173: 531–536.
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Chen, B., Liu, H. & Bao, Z. An Efficient Kernel Optimization Method for Radar High-Resolution Range Profile Recognition. EURASIP J. Adv. Signal Process. 2007, 049597 (2007). https://doi.org/10.1155/2007/49597
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DOI: https://doi.org/10.1155/2007/49597