DocumentCode :
796335
Title :
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
Link To Document :
بازگشت