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Least squares support vector machines for direction of arrival estimation

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3 Author(s)
Rohwer, J.A. ; Sandia Nat. Labs., Albuquerque, NM, USA ; Abdallah, C.T. ; Christodoulou, C.G.

Machine learning research has largely been devoted to binary and multiclass problems relating to data mining, text categorization, and pattern/facial recognition. Recently, popular machine learning algorithms, including support vector machines (SVM), have successfully been applied to wireless communication problems. The paper presents a multiclass least squares SVM (LS-SVM) architecture for direction of arrival (DOA) estimation as applied to a CDMA cellular system. Simulation results show a high degree of accuracy, as related to the DOA classes, and prove that the LS-SVM DDAG (decision directed acyclic graph) system has a wide range of performance capabilities. The multilabel capability for multiple DOAs is discussed. Multilabel classification is possible with the LS-SVM DDAG algorithm presented.

Published in:

Antennas and Propagation Society International Symposium, 2003. IEEE  (Volume:1 )

Date of Conference:

22-27 June 2003

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