An estimation-range extended autocorrelation-based frequency estimator

Authors:
Cui Yang;Gang Wei;Fang-Jiong Chen
Affiliations:
School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China;School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China;School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
Venue:
EURASIP Journal on Advances in Signal Processing
Year:
2009

Citing 4
Cited 3

Performance analysis of a correlation based single tone frequency estimator

Signal Processing
Frequency estimation from proper sets of correlations

IEEE Transactions on Signal Processing
An iterative algorithm for single-frequency estimation

IEEE Transactions on Signal Processing
Iterative frequency estimation by interpolation on Fourier coefficients

IEEE Transactions on Signal Processing

Closed-form frequency estimator based on narrow-band approximation under noisy environment

Signal Processing
A closed-form expanded autocorrelation method for frequency estimation of a sinusoid

Signal Processing
Computationally Efficient Single-Frequency Estimation Scheme in High-Rate WPAN Systems

Wireless Personal Communications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

We address the problem of autocorrelation-based single-tone frequency estimation. It has been shown that the frequency can be estimated from the phase of the available signal's autocorrelation with fixed lag. A large lag results in better performance but at the same time limits the estimation range. New methods have been proposed to extend the estimation range. In this paper, a new estimator which is a robust hybrid of periodogram-based and autocorrelation-based frequency estimators is presented. We propose to calculate the autocorrelation function with spectral lines inside the available signal's main lobe spectrum. We show that the new estimator obtains full estimation range of [-Π, Π]. The theoretical performance bound is also deduced. Performance analysis and simulations demonstrate that the proposed estimator approaches the CRLB.