Two Novel Autocorrelation Based Methods for Frequency Estimation of Real Sinusoid Signal

Kai WANG  Man ZHOU  Lin ZHOU  Jiaying TU  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E102-A   No.4   pp.616-623
Publication Date: 2019/04/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E102.A.616
Type of Manuscript: PAPER
Category: Digital Signal Processing
frequency estimation,  autocorrelation function,  least squares,  reformulation of Pisarenko's method,  

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Many autocorrelation-based frequency estimation algorithms have been proposed. However, some of them cannot construct a strict linear prediction (LP) property among the adjacent autocorrelation lags, which affects the estimators' performance. To improve the precision of frequency estimation, two novel autocorrelation based frequency estimation methods of the real sinusoid signal in additive white Gaussian noise (AWGN) are proposed in this paper. Firstly, a simple method is introduced to transform the real sinusoid signal into the noncircular signal. Secondly, the autocorrelation of the noncircular signal is analyzed and a strict LP property is constructed among the adjacent autocorrelation lags of the noncircular signal. Thirdly, the least squares (LS) and reformed Pisarenko harmonic decomposer (RPHD) frameworks are employed to improve estimation accuracy. The simulation results match well with the theoretical values. In addition, computer simulations demonstrate that the proposed algorithm provides high estimation accuracy and good noise suppression capability.