Multichannel ARMA signal information fusion wiener estimator based on Kalman filtering

Author

Zhuo, Chen ; Shuli, Sun

Author_Institution

Dept. of Autom., Heilongjiang Univ., Harbin

fYear

2008

fDate

2-4 July 2008

Firstpage

4571

Lastpage

4575

Abstract

Using Kalman filtering theory, based on the autoregressive moving average (ARMA) innovation model, the white noise estimator and the measurement predictor, a distributed information fusion Wiener estimator is presented for multichannel ARMA signal with multiple sensors by the matrix weighting fusion algorithm in the linear minimum variance sense. It has the asymptotical stability. It can handle the filtering, smoothing and prediction problems in a unified framework. A simulation example verifies its effectiveness.

Keywords

Kalman filters; asymptotic stability; autoregressive moving average processes; estimation theory; matrix algebra; prediction theory; sensor fusion; smoothing methods; white noise; Kalman filtering theory; asymptotical stability; autoregressive moving average innovation model; distributed information fusion Wiener estimator; matrix weighting fusion algorithm; measurement predictor; prediction problem; smoothing problem; white noise estimator; Autoregressive processes; Filtering theory; Information filtering; Information filters; Kalman filters; Noise measurement; Predictive models; Sensor fusion; Technological innovation; White noise; Kalman filtering theory; Multichannel ARMA signal; Wiener estimator; distributed information fusion; multisensor;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference, 2008. CCDC 2008. Chinese

Conference_Location

Yantai, Shandong

Print_ISBN

978-1-4244-1733-9

Electronic_ISBN

978-1-4244-1734-6

Type

conf

DOI

10.1109/CCDC.2008.4598195

Filename

4598195