Research, Self-tuning Information Fusion Filtering Method Based On Riccati Equation

One of the important methods of Multisensor information fusion is to detect the same target by multiple sensors, and then the local state estimates can be obtained ,under certain optimal fusion rules, by combining or weighting the local estimates, the optimal fusion estimates can be obtained, whose accuracy is higher than local estimates.Self-tuning information fusion filtering is used to deal with information fusion filtering problems for the multisensor systems with the unknown model parameters and noise statistics, it is a frontier feild between optimal information fusion filtering and system identification, so it has important theoretical and applied significance.For the multisensor systerms with unknown model parameters and noise statistics,using recursive instrumental variable (RIV) algorithm and solving correlation function matrix equations,the model parameters estimators and noise statistics estimators are obtained. For the multisensor systerms with correlated measurement noises and unknown noise statistics,using the classical Kalman filtering method, based on the Riccati equation, under the linear minimum variance optimal information fusion criterion weighted by scales for components, the self-tuning component decoupled information fusion Kalman and Wiener estimators are presented respectively. For the AR signals with unknown model parameters and noise statistics, a self-tuning information fusion Wiener filter is presented. The convergence of the self-tuning information fusion estimators is proved by the dynamic error system analysis(DESA) method. Several simulation examples of tracking system show their effectiveness.