Chirp transform in the nonlinear tracking performance analysis of the LMS adaptive predictors

Author

Han, Jun ; Zeidler, James R. ; Ku, Walter H.

Author_Institution

Dept. of Electr. & Comput. Eng., California Univ., San Diego, La Jolla, CA, USA

Volume

1

fYear

2001

fDate

4-7 Nov. 2001

Firstpage

667

Abstract

A chirp transform is defined for the /spl Delta/-step LMS adaptive predictors for linearly chirped signals embedded in additive white Gaussian noise. By converting the chirped signals to stationary baseband signals, this transform provides a different approach in analyzing the tracking performance of the LMS adaptive predictors. This transform also provides an approach for analyzing the nonlinear effects of the LMS adaptive predictor for nonstationary input signals. It is also shown that the chirp transform can be applied to the 1-step RLS predictor with chirped input signals.

Keywords

AWGN; adaptive estimation; chirp modulation; least mean squares methods; nonlinear estimation; prediction theory; tracking; /spl Delta/-step LMS adaptive predictors; 1-step RLS predictor; additive white Gaussian noise; chirp transform; linearly chirped signals; nonlinear effects; stationary baseband signals; tracking performance; AWGN; Adaptive filters; Additive white noise; Baseband; Chirp; Least squares approximation; Performance analysis; Signal analysis; Signal processing; Steady-state;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 2001. Conference Record of the Thirty-Fifth Asilomar Conference on

Conference_Location

Pacific Grove, CA, USA

ISSN

1058-6393

Print_ISBN

0-7803-7147-X

Type

conf

DOI

10.1109/ACSSC.2001.987008

Filename

987008