Estimating Frequency by Interpolation Using Least Squares Support Vector Regression

Abstract EN

Discrete Fourier transform- (DFT-) based maximum likelihood (ML) algorithm is an important part of single sinusoid frequency estimation.

As signal to noise ratio (SNR) increases and is above the threshold value, it will lie very close to Cramer-Rao lower bound (CRLB), which is dependent on the number of DFT points.

However, its mean square error (MSE) performance is directly proportional to its calculation cost.

As a modified version of support vector regression (SVR), least squares SVR (LS-SVR) can not only still keep excellent capabilities for generalizing and fitting but also exhibit lower computational complexity.

In this paper, therefore, LS-SVR is employed to interpolate on Fourier coefficients of received signals and attain high frequency estimation accuracy.

Our results show that the proposed algorithm can make a good compromise between calculation cost and MSE performance under the assumption that the sample size, number of DFT points, and resampling points are already known.