Optimal weights for autocorrelation sequences

Author

Adams, John W. ; Sucher, Robert W. ; Sullivan, James L. ; Gleeson, David

Author_Institution

Sch. of Eng. & Comput. Sci., California State Univ., Northridge, CA, USA

fYear

1993

fDate

1-3 Nov 1993

Firstpage

1255

Abstract

Weighting functions for autocorrelation sequences are discussed in most signal processing textbooks. The standard design method is to convolve a classic window with itself. This method guarantees non-negative power spectral estimates, but it leads to suboptimal performance. In this paper we will present approaches to designing optimal autocorrelation windows. The optimization problems were formulated both in terms of quadratic programming and linear programming

Keywords

correlation theory; linear programming; quadratic programming; signal processing; spectral analysis; autocorrelation sequences; convolving; linear programming; nonnegative power spectral estimates; quadratic programming; signal processing; suboptimal performance; weighting functions; Amplitude estimation; Autocorrelation; Computer science; Design methodology; Fourier transforms; Frequency; Linear programming; Optimization methods; Power engineering and energy; Quadratic programming; Robustness; Signal processing;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 1993. 1993 Conference Record of The Twenty-Seventh Asilomar Conference on

Conference_Location

Pacific Grove, CA

ISSN

1058-6393

Print_ISBN

0-8186-4120-7

Type

conf

DOI

10.1109/ACSSC.1993.342334

Filename

342334