Scalar weighting optimal fusion predictors for discrete multichannel ARMA signals

Author

Sun, Shuli

Author_Institution

Dept. of Autom., Heilongjiang Univ., Harbin, China

Volume

3

fYear

2004

fDate

6-9 Dec. 2004

Firstpage

1626

Abstract

Based on the multi-sensor optimal information fusion criterion weighted by scalars in the linear minimum variance sense, the distributed optimal fusion Kalman multi-step predictor is given for discrete multi-channel ARMA (autoregressive moving average) signals. The precision of the fusion predictor is higher than that of any local predictor. It only requires the computation of scalar weights, the computational burden can be reduced comparing with one weighted by matrices. An example of double-channel signal system with three sensors shows the effectiveness.

Keywords

autoregressive moving average processes; iterative methods; prediction theory; sensor fusion; autoregressive moving average; discrete multichannel ARMA signals; distributed optimal fusion Kalman multistep predictor; linear minimum variance; multisensor optimal information fusion criterion; scalar weighting optimal fusion predictors; Automation; Estimation error; Fuses; Kalman filters; Maximum likelihood estimation; Sensor fusion; Sensor systems; Sun; White noise; Working environment noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Control, Automation, Robotics and Vision Conference, 2004. ICARCV 2004 8th

Print_ISBN

0-7803-8653-1

Type

conf

DOI

10.1109/ICARCV.2004.1469303

Filename

1469303