Title :
Multisensor Information Fusion Wiener Deconvolution Predictor
Author :
Lin, Mao ; Zili, Deng
Author_Institution :
Harbin Eng. Univ., Harbin
Abstract :
By the modern time series analysis methods, based on ARMA innovation model and augmented state space model, a multisensor optimal information fusion Wiener deconvolution predictor weighted by scalars is proposed. The formulas of computing the local predictor error variances and cross-covariances are given, which are applied to compute optimal weighting coefficients. Compared to the single sensor case, the accuracy of the fused predictor is improved. A simulation example shows its effectiveness.
Keywords :
autoregressive moving average processes; deconvolution; sensor fusion; state-space methods; ARMA innovation model; Wiener deconvolution predictor; cross-covariance; error variance; multisensor information fusion; optimal information fusion; optimal weighting coefficient; state space model; time series analysis; Deconvolution; Deconvolution; Multisensor Information Fusion; Optimal Fusion Rule Weighted by Scalars; Wiener Deconvolusion Predictor;
Conference_Titel :
Control Conference, 2007. CCC 2007. Chinese
Conference_Location :
Hunan
Print_ISBN :
978-7-81124-055-9
Electronic_ISBN :
978-7-900719-22-5
DOI :
10.1109/CHICC.2006.4346816
