Least-squares Support Vector Machines For Doa Estimation: A Step-by-step Description And Sensitivity Analysis

dc.creatorLima C.A.M.
dc.creatorJunqueira C.
dc.creatorSuyama R.
dc.creatorVon Zuben F.J.
dc.creatorRomano J.M.T.
dc.date2005
dc.date2015-06-26T14:10:03Z
dc.date2015-11-26T14:10:05Z
dc.date2015-06-26T14:10:03Z
dc.date2015-11-26T14:10:05Z
dc.date.accessioned2018-03-28T21:10:42Z
dc.date.available2018-03-28T21:10:42Z
dc.identifier0780390482; 9780780390485
dc.identifierProceedings Of The International Joint Conference On Neural Networks. , v. 5, n. , p. 3226 - 3231, 2005.
dc.identifier
dc.identifier10.1109/IJCNN.2005.1556444
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-33750125972&partnerID=40&md5=8939a6868bdb5b4d530b149fb94907c3
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/93947
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/93947
dc.identifier2-s2.0-33750125972
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1241415
dc.descriptionAdaptive beamforming in antenna arrays aims at adjusting the weighted linear combination of the output signals provided by the antennas so that the power of the received signals at dominant paths is maximized at the same time that the power of interference and noise signals is minimized. The weight vectors, each one associated with one received signal, can be directly obtained if the direction of arrival (DOA) of the corresponding signal has already been estimated. The process of DOA estimation involves the prediction of the angle of arrival by means of monitoring the output produced by the antennas in the array, given that the number of antennas is higher than the number of signals to be detected. Even though signal subspace techniques have made a good job in DOA estimation, they present some important drawbacks that will be alleviated here using a supervised learning approach, in the form of a multiclass LS-SVM classification problem. The main contribution of this paper is twofold: a step-by-step description of the complete set of algebraic manipulation for data preprocessing and for the synthesis of the classification device, and an analysis of the effect in performance when relevant parameters vary in a given operational interval. © 2005 IEEE.
dc.description5
dc.description
dc.description3226
dc.description3231
dc.descriptionLiberti Jr., J.C., Rappaport, T.S., Smart antennas for wireless communications (1999) IS-95 and Third Generation CDMA Applications, , New Jersey: Prentice Hall
dc.descriptionSchimidt, R.O., Multiple emitter location and signal parameter estimation (1979) Proc. of RADC Spectrum Estimation Workshop, Griffiss AFB, pp. 243-258. , N.Y
dc.descriptionSchimidt, R.O., Multiple emitter location and signal parameter estimation (1986) IEEE Trans. on Antennas and Propagation, AP-34 (3), pp. 281-290. , Mar
dc.descriptionRoy, R., Kailath, T., ESPRIT - Estimation pf signal parameters via rotational invariance techniques (1990) Optical Engineering, 29 (4), pp. 296-313. , Apr
dc.descriptionRohwer, J.A., (2003) Learning Methods for CDMA Power Control and Direction of Arrival Estimation, , Ph.D. Thesis, The University of New Mexico. Albuquerque, New Mexico, May
dc.descriptionRohwer, J.A., Abdallah, C.T., Christodoulou, C.G., Least square support vector machines for direction of arrival estimation with error control and validation (2003) IEEE 2003 Global Telecommunications Conference, pp. 2172-2176
dc.descriptionChristodoulou, C.G., Rohwer, J.A., Abdallah, C.T., The use of machine learning in smart antennas (2004) IEEE Antennas and Propagation Society Symposium, pp. 321-324
dc.descriptionRappaport, T.S., (1996) Wireless Communication: Principles and Practice, , New Jersey. Prentice Hall
dc.descriptionJunqueira, C.C.M., (2003) Schemes and Algorithms for Adaptive Arrays Applied to GPS, 244p. , PhD. Thesis, State University of Campinas, FEEC, UNICAMP (in Portuguese)
dc.descriptionDandekar, K.R., Ling, H., Xu, G., Fye, D., Smart antenna array calibration procedure including amplitude and phase mismatch and mutual coupling effects (2000) IEEE International Conference on Personal Wireless Communication
dc.descriptionTyler, N., Alien, B., Aghvami, H., Adaptive antennas: The calibration problem (2004) IEEE Communications Magazine, 42 (12), pp. 114-122. , Dec
dc.descriptionVapnik, V.N., (1998) Statistical Learning Theory, , John Willey & Sons
dc.descriptionVapnik, V.N., (1995) The Nature of Statistical Learning Theory, , Springer-Verlag
dc.descriptionSchölkopf, B., Sung, K., Burges, C., Girosi, F., Niyogi, P., Vapnik, V.N., Comparing support vector machines with Gaussian kernels to radial basis function classifiers (1997) IEEE Transactions on Signal Processing, 45 (11), pp. 2758-2765
dc.description(1998) Advance in Kernel Methods - Support Vector Learning, , B. Schölkopf, C. Burges, C.
dc.descriptionSmola, (Eds.), MIT Press
dc.description(1998) Nonlinear Modeling: Advance Black-box Techniques, , J. A. K. Suykens, J. Vanderwalle, (Eds.), Kluwer Academic Publishers, Boston
dc.descriptionHaykin, S., (1999) Neural Networks: A Comprehensive Foundation, , Prentice Hall, 2nd. edition
dc.descriptionBishop, C.M., (1995) Neural Networks for Pattern Recognition, , Oxford University Press
dc.descriptionSuykens, J.A.K., Vanderwalle, J., Least squares support vector machine classifiers (1999) Neural Processing Letters, 9 (3), pp. 293-300
dc.descriptionSuykens, J.A.K., Van Gestel, T., De Brabater, J., De Moor, B., Vanderwalle, J., (2002) Least Squares Support Vector Machines, , World Scientific
dc.descriptionSaunders, C., Gammerman, A., Vovk, V., Ridge regression learning algorithm in dual variables (1998) Proc. of the 15th Int. Conf. on Machine Learning (ICML '98), pp. 515-521. , Morgan Kaufmann
dc.descriptionFletcher, R., (1987) Pratical Methods of Optimization, , New York: Wiley
dc.descriptionSmola, A., (1999) Learning with Kernels, , Ph.D. Thesis, published by: GMD, Birlinghoven
dc.descriptionSuykens, J.A.K., Lukas, L., Van Dooren, P., De Moor, B., Vanderwalle, J., Least squares support vector machine classifiers: A large scale algorithm (1999) European Conference on Circuit Theory and Design, (ECCTD '99), pp. 839-842. , Stresa Italy, August
dc.descriptionPlatt, J.C., Cristianini, N., Shawe-Taylor, J., Large margin DAGs for multiclass classification (2000) Advances in Neural Information Processing Systems, 12, pp. 547-553. , MIT Press
dc.languageen
dc.publisher
dc.relationProceedings of the International Joint Conference on Neural Networks
dc.rightsfechado
dc.sourceScopus
dc.titleLeast-squares Support Vector Machines For Doa Estimation: A Step-by-step Description And Sensitivity Analysis
dc.typeActas de congresos


Este ítem pertenece a la siguiente institución