Optimal Component Fusion Steady-State Smoothing for Discrete Multichannel ARMA Signals

Author

Sun, Shuli

Author_Institution

Dept. of Autom., Heilongjiang Univ., Harbin

Volume

1

fYear

0

fDate

0-0 0

Firstpage

1438

Lastpage

1441

Abstract

Based on white noise estimators and the optimal fusion algorithm in the LMV (linear minimum variance) sense, distributed optimal fusion steady-state smoothers with scalar weights are given for all components of discrete multichannel ARMA (autoregressive moving average) signals with correlated noises. The precision of the fusion smoothers is higher than that of local smoothers, but is lower than that of the fusion smoother with matrix weights. However, the computational burden can be reduced since only scalar weights are required. Applying it to a double-channel ARMA signal system with three sensors shows the effectiveness

Keywords

autoregressive moving average processes; sensor fusion; white noise; autoregressive moving average signals; correlated noises; discrete multichannel ARMA signals; distributed optimal fusion; linear minimum variance; steady-state smoothing; white noise estimators; Filters; Gaussian distribution; Laboratories; Maximum likelihood estimation; Noise measurement; Sensor systems; Smoothing methods; Steady-state; Sun; White noise; ARMA signal; Multisensor; component fusion smoother; information fusion;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1712586

Filename

1712586