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Estimation and classification of polynomial-phase signals

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TLDR
A novel new integral transform that is adapted for signals of this type is introduced and used to derive estimation and classification algorithms that are simple to implement and that exhibit good performance.
Abstract
The measurement of the parameters of complex signals with constant amplitude and polynomial phase, measured in additive noise, is considered. A novel new integral transform that is adapted for signals of this type is introduced. This transform is used to derive estimation and classification algorithms that are simple to implement and that exhibit good performance. The algorithms are extended to constant amplitude and continuous nonpolynomial phase signals. >

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Citations
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Journal ArticleDOI

Analysis of multicomponent LFM signals by a combined Wigner-Hough transform

TL;DR: The overall method is shown to be asymptotically efficient and offers a good rejection capability of the cross terms and a closed form expression is found for the signal-to-noise ratio and the parameter estimation accuracy.
Journal ArticleDOI

The discrete polynomial-phase transform

TL;DR: The authors define the discrete polynomial-phase transform, derive its basic properties, and use it to develop computationally efficient estimation and detection algorithms.
Journal ArticleDOI

Product high-order ambiguity function for multicomponent polynomial-phase signal modeling

TL;DR: The use of the PHAF offers a number of advantages with respect to the high-order ambiguity function (HAF), and removes the identifiability problem and improves noise rejection capabilities.
Journal ArticleDOI

Discrete chirp-Fourier transform and its application to chirp rate estimation

TL;DR: An upper bound for the number of the detectable chirp components using the DCFT is provided in terms of signal length and signal and noise powers, and it is shown that the N-point DCFT performs optimally when N is a prime.
Journal ArticleDOI

The Cramer-Rao lower bound for signals with constant amplitude and polynomial phase

TL;DR: The authors derive the Cramer-Rao lower bound (CRLB) for complex signals with constant amplitude and polynomial phase, measured in additive Gaussian white noise, which is found to be excellent in most cases.
References
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Journal ArticleDOI

Single tone parameter estimation from discrete-time observations

TL;DR: Estimation of the parameters of a single-frequency complex tone from a finite number of noisy discrete-time observations is discussed and appropriate Cramer-Rao bounds and maximum-likelihood estimation algorithms are derived.
Journal ArticleDOI

The Cramer-Rao lower bound for signals with constant amplitude and polynomial phase

TL;DR: The authors derive the Cramer-Rao lower bound (CRLB) for complex signals with constant amplitude and polynomial phase, measured in additive Gaussian white noise, which is found to be excellent in most cases.
Journal ArticleDOI

Linear FM signal parameter estimation from discrete-time observations

TL;DR: In this article, the authors considered the problem of estimating the parameters of a complex linear FM signal from a finite number of noisy discrete-time observations, and proposed an estimation algorithm consisting of two fast Fourier transforms (FFTs) accompanied by one-dimensional searches for maxima.
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