Title :
Scalar weighting optimal fusion predictors for discrete multichannel ARMA signals
Author_Institution :
Dept. of Autom., Heilongjiang Univ., Harbin, China
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;
Conference_Titel :
Control, Automation, Robotics and Vision Conference, 2004. ICARCV 2004 8th
Print_ISBN :
0-7803-8653-1
DOI :
10.1109/ICARCV.2004.1469303
