Efficient multiscale stochastic realization

Author

Frakt, Austin B. ; Willsky, Alan S.

Author_Institution

Lab. for Inf. & Decision Syst., MIT, Cambridge, MA, USA

Volume

4

fYear

1998

fDate

12-15 May 1998

Firstpage

2249

Abstract

Few fast statistical signal processing algorithms exist for large problems involving non-stationary processes and irregular measurements. A previously introduced class of multiscale autoregressive models indexed by trees admits signal processing algorithms which can efficiently deal with problems of this type. In this paper we provide a novel and efficient algorithm for translating any second-order prior model to a multiscale autoregressive prior model so that these efficient signal processing algorithms may be applied

Keywords

autoregressive processes; signal processing; trees (mathematics); efficient multiscale stochastic realization; fast statistical signal processing algorithms; irregular measurements; large problems; multiscale autoregressive models; multiscale autoregressive prior model; nonstationary processes; second-order prior model; translation; trees; Error analysis; Estimation error; Fast Fourier transforms; Fuses; Laboratories; Least squares approximation; Noise measurement; Signal processing; Signal processing algorithms; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on

Conference_Location

Seattle, WA

ISSN

1520-6149

Print_ISBN

0-7803-4428-6

Type

conf

DOI

10.1109/ICASSP.1998.681596

Filename

681596