System With Different Local Dynamic Models Of Time-varying Kalman Fusion And Its Application

Multisensor information fusion is also called multisensor data fusion, it means toprocess the data from many sensors data with various and multistage in order to producenewly significant information, and the new information are not obtained from any singlesensor. It avoids the single sensor limitation, and can obtain more information to getmore accurate and reliable conclusion. Information fusion technology involves theoverlapping and concrete utilization of many subjects, such as mathematics, militaryscience, computer science, automatic control theory, artificial intelligence,communication, management science, and so on. It uses the complementation ofpleophyletic data and highly speed operation performance of the computer fully, andimproves the information processing quality effectively.For linear discrete time-varying stochastic control systems with different localdynamic models, using the Kalman filtering method, based on the Riccatci equations,under the linear minimum variance optimal fusion criterion, according to three optimalfusion rules weighted by matrices, diagonal matrices, and scalars, the three optimalweighted fusion Kalman estimators are presented for the common state. They canhandle the fused filtering, prediction, and smoothing problems in a unified framework.In order to compute the optimal weights, the formulas of computing the local estimationerror cross-covariances are proposed. They can be applied to signal fused filtering. Bythe augmented state approach, the signal to be estimated can be viewed as a commonstate of the subsystems, so that the information fusion estimators for the ARMA signalwith white and color observation noises and multisensor is presented, and thecorresponding information fusion problem under the steady-state is solved. Themultisensor time-varying ARMA signal information fusion deconvolution estimator ispresented too. Many Monte-Carlo simulation examples in tracking systems and the numerical Monte-Carlo simulation examples show their effectiveness.