Using Least Squares Support Vector Machines for Frequency Estimation

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DOI: 10.4236/ijcns.2010.310111   PDF   HTML     4,859 Downloads   8,774 Views  

Abstract

Frequency estimation is transformed to a pattern recognition problem, and a least squares support vector machine (LS-SVM) estimator is derived. The estimator can work efficiently without the need of statistics knowledge of the observations, and the estimation performance is insensitive to the carrier phase. Simulation results are presented showing that proposed estimators offer better performance than traditional Maximum Likelihood (ML) estimator at low SNR, since classification-based method does not have the threshold effect of nonlinear estimation.

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X. Teng, X. Zhang and H. Yu, "Using Least Squares Support Vector Machines for Frequency Estimation," International Journal of Communications, Network and System Sciences, Vol. 3 No. 10, 2010, pp. 821-825. doi: 10.4236/ijcns.2010.310111.

Conflicts of Interest

The authors declare no conflicts of interest.

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