Information Fusion Estimation Theory And Its Applications In Satellite State Estimation

Information fusion estimation theory, as a basic problem in control and signal processing regions also is an important theory basis for information fusion theory, which has been researched widely at present. Combining information fusion technology and state estimation theory to develop state fusion estimation method based on information fusion is a research direction of information fusion, which is the only way to obtain the state estimation result with high precision and high reliability.The research work in this paper focuses on enriching and developing multi-sensor information fusion estimation theory and analyzes the existing problems of this researched theory when being applied in actual multi-sensor system and then presents the corresponding resolution methods. The contributions and innovation points are as follows:1. Systematically analyzing state fusion estimation theory in standard multi-sensor information fusion system: linear optimal weighting and state fusion estimation are analyzed; influence relationship between weighting value and precision of optimal fusion estimation is discussed. The expressions of linear minimum square fusion estimation (LMSE) and weighting least square fusion estimation (WLSE) are presented. Combined with Bayes theory, a Bayes statistic fusion model based on minimizing the variance for the improved state to be fused is established. Filter theory and algorithm for standard multi-sensor dynamical system are discussed; the expressions of fusion estimation for dynamical system in different fusion structures are advanced. The unified linear fusion model for multi-sensor information fusion system is presented and then estimation performance for optimal state fusion is analyzed.2. Detailedly researching state fusion estimation in some typical nonstandard multi-sensor information fusion system existed in actual application: three resolution schemes based on parameter and semi-parameter modeling, multi model fusion and self-adaptive estimation are relatively advanced, and moreover, the corresponding fusion estimation model and algorithm are presented.(1) Aiming at some uncertainty and nonlinear factors in actual multi-sensor fusion system, two modeling methods respectively based on wavelet time-sequence modeling and semi-parameter modeling are researched to solve state fusion estimation in nonstandard multi-sensor fusion system. For the former, the process is to extract the character of dynamical system and then transfers the problem for treating uncertainty into parameter estimation problem with linear or nonlinear analysis model by establishing mathematical model with parameter expression. For the latter, the process is to separate model error brought by nonlinear and uncertainty factors with semi-parameter modeling method and then weakens the influence to the state fusion estimation precision.(2) Aiming at some nonlinearity and time-change existed in nonstandard multi-sensor fusion system, two multi-model fusion estimation methods respectively based on model probability and model curvature are researched to approach system dynamical character. For the former, the process is to approach the complex and nonlinear time-change process by using of multi linear models; two expressions for model probability are designed and the corresponding multi-model fusion estimation algorithm is established. For the latter, the process is to introduce curvature matrix to evaluate nonlinearity degree of parameter estimation and then to determine the selection rule for optimal fusion weighting value; the optimal weighting theory and the corresponding parameter estimation algorithm for multi-model and nonlinear system are established.(3) Aiming at local dependency and system parameter uncertainty existed in multi-sensor dynamical system, taking Kalman filter as theory basis, optimal estimation theory, federal filter theory, self-adaptive control theory and neural network are applied for state fusion estimation in nonstandard multi-sensor dynamical system, a multi-information federal filter based on information distribution and a multi-information self-adaptive fusion estimation algorithm based neural network are respectively advanced. For the former, fusion strategies with dependency or independency for local filters are presented; the estimation performance for federal filter is analyzed and a self-adaptive determination method for information distribution factor is researched. For the latter, a paralleled fusion estimation model based on neural network and the corresponding self-adaptive study algorithm for neuron fusion weight based UKF (Unscented Kalman Filter) are presented.3. Combining with the application actuality for multi-sensor information fusion theory in spaceflight region, the application research of fusion estimation theory in satellite orbit and attitude determination is developed; According to some standard and nonstandard multi-sensor fusion estimation methods researched above, a modeling method with time-sequence analysis for satellite perturbing force based on wavelet transform, a fusion estimation method for satellite orbit determination based on semi-parameter regression model, a fusion estimation method for satellite orbit determination based on Bayes statistic model, a multiple model fusion method for satellite orbit determination based on model probability, a fusion estimation method for satellite attitude determination based on multi-sensor measurement and a fusion estimation method with neural networks for multi-sensor measurement of satellite attitude determination based on UKF are respectively constructed and simulated. The experiment results all indicate that the researched fusion estimation models and algorithms can restrain each nonstandard factor existed in multi-sensor information fusion system to the effect of satellite state estimation performance.