Signal-to-noise ratio estimation using higher-order moments
Signal Processing
Instantaneous frequency estimation using stochastic calculus and bootstrapping
EURASIP Journal on Applied Signal Processing
Time-frequency analysis using warped-based high-order phase modeling
EURASIP Journal on Applied Signal Processing
Performance of instantaneous frequency rate estimation using high-order phase function
IEEE Transactions on Signal Processing
Are genetic algorithms useful for the parameter estimation of FM signals?
Digital Signal Processing
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The high-order ambiguity function (HAF) is a nonlinear operator designed to detect, estimate, and classify complex signals whose phase is a polynomial function of time. The HAF algorithm, introduced by Peleg and Porat (1991), estimates the phase parameters of polynomial-phase signals measured in noise. The purpose of this correspondence is to analyze the asymptotic accuracy of the HAF algorithm in the case of additive white Gaussian noise. It is shown that the asymptotic variances of the estimates are close to the Cramer-Rao bound (CRB) for high SNR. However, the ratio of the asymptotic variance and the CRB has a polynomial growth in the noise variance