Optimal And Self-tuning Multisensor Information Fusion White Noise Deconvolution Estimators

With the coming of information era, multisensor information fusion is being emphasized and applied widely in the world due to the effectively improved and optimized performance of estimation, identification and detection (control) based on the single sensor. The applications spread over various aspects of military and civil fields. As an embranchment, optimal and self-tuning information fusion filtering theory refers to the fusion estimation problem of state or signal for the multisensor systems with known or unknown model parameters and/or noise statistics, respectively. The input white noise estimation problem of systems or white noise deconvolution estimation problem has important applications in seismic exploration and communication systems.Using two methodologies—Kalman filtering method and modern time series analysis method, this paper researches the optimal and self-tuning multisensor information fusion white noise deconvolution estimators based on two fusion methods, so-called multisensor weighted state fusion and weighted measurement fusion methods, combining with the system identification methods, respectively. The main work includes the following four aspects:Firstly, based on the Riccati equation, the unified weighted fusion optimal and steady-state optimal white noise estimators are presented using Kalman filtering method for the multisensor systems with different local dynamic models and correlated noises. In order to compute the optimal weights, two formulae of computing the local estimation error cross-covariances are given.Secondly, based on the ARMA innovation model, the unified steady-state optimal white noise estimators are presented using the modern time series analysis method for the multisensor systems with different local dynamic models and correlated noises. The optimal weighted state fusion white noise deconvolution estimators are presented for the multisensor time-delayed systems with the same or different local dynamic models. In order to compute the optimal weights, the formulae of computing the local estimation error cross-covariances are given, respectively.Thirdly, based on the Riccati equation, the optimal weighted measurement fusion white noise deconvolution estimators are respectively presented using the Kalman filtering method for the multisensor time-varying systems with the same measurement matrices and correlated measurement noises or with different measurement and correlated measurement noises or with the same measurement matrices and correlated noises. Moreover, the completely functional equivalence and the global optimality of the fusers are proved compared with the corresponding centralized fusion white noise deconvolution estimators. As a special case, the corresponding steady-state optimal weighted measurement fusion white noise deconvolution estimators are also given for the time-invariant systems.Finally, based on the Riccati equation, the self-tuning weighted measurement fusion white noise deconvolution estimators are presented using the Kalman filtering method for the multisensor time-invariant systems with unknown noise statistics, where the estimators of information fusion noise statistics by the correlation function method are used. For the multisensor single channel AR or ARMA systems with unknown model parameters and noise statistics, the self-tuning weighted measurement fusion white noise deconvolution estimators are presented using the estimators of model parameters and noise statistics by the correlation function method, recursive instrumental variable algorithm and Gevers-Wounters algorithm. Moreover, based on the Dynamic Error System Analysis method, it is proved that the fusers converge to the corresponding steady-state optimal weighted measurement fusion white noise deconvolution estimators, so that they have the asymptotical global optimality;The above results are all proved by the simulation examples, which show the effectiveness of the theory. They have important theoretical and application value in many fields including the multisensor information fusion filtering, oil seismic exploration, signal processing, and state estimation.