Asymptotic statistical analysis of the high-order ambiguity function for parameter estimation of polynomial-phase signals

Author

Porat, Boaz ; Friedlander, Benjamin

Author_Institution

Dept. of Electr. Eng., Technion-Israel Inst. of Technol., Haifa, Israel

Volume

42

Issue

3

fYear

1996

fDate

5/1/1996 12:00:00 AM

Firstpage

995

Lastpage

1001

Abstract

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

Keywords

Gaussian noise; interference (signal); phase estimation; polynomials; signal detection; statistical analysis; white noise; Cramer-Rao bound; HAF algorithm; additive white Gaussian noise; asymptotic statistical analysis; asymptotic variance; high SNR; high-order ambiguity function; noise variance; nonlinear operator; parameter estimation; polynomial function; polynomial growth; polynomial-phase signals; signal classification; signal detection; Noise measurement; Parameter estimation; Phase detection; Phase estimation; Phase measurement; Phase noise; Polynomials; Signal design; Signal to noise ratio; Statistical analysis;

fLanguage

English

Journal_Title

Information Theory, IEEE Transactions on

Publisher

ieee

ISSN

0018-9448

Type

jour

DOI

10.1109/18.490563

Filename

490563