Self-tuning Information Fusion Status And Signal Wiener Estimators

Recently, with the development of computer and communication technology, multisensor information fusion technology develops rapidly and becomes an active research focus in current information domain. Multisensor information fusion is also called multisensor data fusion, which uses multisensor to detect the same object, avoids the limitation of single sensor, so it can offer more general and accurate information. As a new technology for multisource date processing, multisensor information fusion can synthesize multisource date from one object, generate more accurate estimation than single source.For multisensor single output systems with unknown model parameters and noise (colored and white noise) statistics, using the model time series analysis method, based on recursive extend least squares method or two-stage recursive extend least squares method of the autoregressive average moving innovation model, the unknown model parameters and noise statistics can be obtained on line. Under the linear minimum variance optimal information fusion criterion weighted by scales for state components, self-tuning decoupled fusion Wiener state estimators for the single output system with unknown colored measurement (or input) noise are presented respectively; under the linear minimum variance optimal information fusion criterion weighted by scales, ARMA signal self-tuning fusion Wiener estimators for the single output system with unknown model parameters or measurement noise are presented respectively. The convergence of them is proved, i.e if the parameters estimation is consistent, then self-tuning fusion estimator will converge to the optimal fusion estimator with known model parameters and noise statistics, and the accuracy of self-tuning fusion estimator is higher than that of each local self-tuning estimator. Many simulation examples of tracking system show their effectiveness.