| Multisensor information fusion is a new technology to process multidimensional information synthetically .It has been applied in the field of information acquisition and processing widely and has become an active research focus in current information domain. With the development of science and technology, a single sensor detection techonology can not satisfy the requirements of detection, so multi-sensor fusion technology came into being. Multi-sensor fusion technology is a technology of detecting a same object, by using all kinds of detection information and different detection approach to object's comprehensive information ,so, improves the accuracy and reliability.Input white noise is also called white noise deconvolution . it has an important applied background in oil seismic exploration.For linear discrete time-invariant stochastic systems with different local models, according to optimal fusion rules weighted by scalars ,using the modern time series analysis method, based on the autoregressive moving average (ARMA) innovation model and white noise estimation theory, an algorithm of the steady state optimal information fusion common input white noise deconvolution estimators are presented. And combine the modem time series analysis method and classical Kalman filtering method, two equivalent algotithms of the steady state optimal information fusion common input white noise deconvolution estimators are presented. Their accuracy is higher than that of each local estimator, and they can handle the fused filtering, prediction, and smoothing problems in a unified framework. In order to compute the optimal weights, the formulas of computing the cross-covariances among local estimation errors of input white noise are given. Especially, they can be applied to solve the white noise deconvolution fused estimation problem for system with color observation noise . Many Monte Carlo simulation examples for input white noise show their effectiveness and correctness. |
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