Title :
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
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;
Conference_Titel :
Signals, Systems and Computers, 1993. 1993 Conference Record of The Twenty-Seventh Asilomar Conference on
Conference_Location :
Pacific Grove, CA
Print_ISBN :
0-8186-4120-7
DOI :
10.1109/ACSSC.1993.342334
