Digital processing of random signals: theory and methods
Digital processing of random signals: theory and methods
Local polynomial Wigner distribution
Signal Processing
Signal-to-noise ratio estimation using higher-order moments
Signal Processing
Multicomponent chirp signals analysis using product cubic phase function
Digital Signal Processing
Time-frequency analysis using warped-based high-order phase modeling
EURASIP Journal on Applied Signal Processing
Adaptive algorithm for chirp-rate estimation
EURASIP Journal on Advances in Signal Processing
Analysis of polynomial-phase signals by the integrated generalizedambiguity function
IEEE Transactions on Signal Processing
Estimating parameters of multiple wideband polynomial-phase sourcesin sensor arrays
IEEE Transactions on Signal Processing
Product high-order ambiguity function for multicomponentpolynomial-phase signal modeling
IEEE Transactions on Signal Processing
Generalized High-Order Phase Function for Parameter Estimation of Polynomial Phase Signal
IEEE Transactions on Signal Processing - Part I
The discrete polynomial-phase transform
IEEE Transactions on Signal Processing
A fast algorithm for estimating the parameters of a quadratic FM signal
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
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The estimation of polynomial-phase signals (PPSs) is a multiparameter problem, and the maximum likelihood (ML) optimization functions have numerous local optima, making the application of gradient techniques impossible. The common solution to this problem is based on the phase differentiation (PD) techniques that reduce the number of dimensions but, at the same time, reduce the accuracy and generate additional difficulties such as spurious components and error propagation. Here we show that genetic algorithms (GAs) can serve as a powerful alternative to the PD techniques. We investigate the limits of accuracy of the ML technique, and of some alternatives such as the high-order cubic phase function (HO-CPF) and high-order Wigner distribution (HO-WD). The ML approach combined with the proposed GA setup is limited up to the fifth-order PPS, which is not sufficient in many applications. However, the HO-CPF and HO-WD techniques coupled with the GA are able to accurately estimate phase parameters up to the tenth-order PPS. They significantly improve the accuracy with respect to the high-order ambiguity function (HAF) and product HAF (PHAF) and, for higher-order PPSs, they are much simpler and more efficient than the integrated generalized ambiguity function (IGAF).