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http://dx.doi.org/10.5573/ieek.2013.50.4.182

Target Classification Algorithm Using Complex-valued Support Vector Machine  

Kang, Youn Joung (Department of Ocean System Engineering, Jeju National University)
Lee, Jaeil (Department of Ocean System Engineering, Jeju National University)
Bae, Jinho (Department of Ocean System Engineering, Jeju National University)
Lee, Chong Hyun (Department of Ocean System Engineering, Jeju National University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.50, no.4, 2013 , pp. 182-188 More about this Journal
Abstract
In this paper, we propose a complex-valued support vector machine (SVM) classifier which process the complex valued signal measured by pulse doppler radar (PDR) to identify moving targets from the background. SVM is widely applied in the field of pattern recognition, but features which used to classify are almost real valued data. Proposed complex-valued SVM can classify the moving target using real valued data, imaginary valued data, and cross-information data. To design complex-valued SVM, we consider slack variables of real and complex axis, and use the KKT (Karush-Kuhn-Tucker) conditions for complex data. Also we apply radial basis function (RBF) as a kernel function which use a distance of complex values. To evaluate the performance of the complex-valued SVM, complex valued data from PDR were classified using real-valued SVM and complex-valued SVM. The proposed complex-valued SVM classification was improved compared to real-valued SVM for dog and human, respectively 8%, 10%, have been improved.
Keywords
SVM; Complex-valued SVM; Classifier; Machine learning;
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Times Cited By KSCI : 3  (Citation Analysis)
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1 이재일, 이주형, 현종우, 이종현, 배진호, 팽동국, 조정삼, 강태인, 이노복, "PDR 센서를 이용한 USN 기반의 감시경보 시스템," 전자공학회논문지, 제48권 TC편, 제12호, 54-61쪽, 2011년 12월   과학기술학회마을
2 Youngwook Kim, Hao Ling, "Human activity classification based on micro-doppler signatures using a support vector machine," IEEE Trans. Geosci. Remote Sens., Vol. 47, no. 5, pp. 1328-1337, May 2009.   DOI   ScienceOn
3 Michael Glen Andersom, "Design of multiple frequency continuous wave radar hardware and micro-doppler based detection and classification algorithms," Ph.D. dissertation, Univ. Texas, Austin, pp. 136-156, May 2008.
4 임정수, 송지현, 장준혁, "SVM 미세조정을 통한 음석/음악 분류 성능향상," 전자공학회논문지, 제48권 SP편, 제2호, 141-148쪽, 2011년 3월
5 Manel Martinez-Ramon, Christos Christodouloui, "Support vector machines for antenna array processing and electromagnetics," Morgan & Claypool, pp. 33-42, 2006.
6 Yasemin Altun, Ioannis Tsochantaridis, Thomas Hofmann, "Hidden markov support vector machines," International conference on machine learning, pp. 3-10, Washington DC, USA, August 2003.
7 Ibrahim Onaran, N. Firat Ince, A. Enis Cetin, Aviva Abosch, "A hybrid SVM/HMM based system for the state detection of individual finger movements from multichannel ECoG signals," International IEEE EMBS conference on neural engineering, pp. 457-460, Cancun, Mexico, May 2011.
8 Dong-Hyuck Seo, Tae-Seong Roh, Dong-Whan Choi, "Defect diagnostics of gas turbine engine using hybrid SVM-ANN with module system in off-design condition," Journal of mechanical science and technology, Vol. 23, No. 3, pp. 677-685, 2009.   DOI   ScienceOn
9 J.L.Rojo-Alvarez, M. Martinez-Ramon, A. R. Figueiras- Vidal, A.Garcia-Armada, A.Artes -Rodriguez, "A robust support vector algorithm for nonparametric spectral analysis," IEEE Transactions on Signal Processing, Vol. 52, No. 1, pp. 155-164, January 2004.   DOI   ScienceOn