Multisensor Information Fusion State Estimator for Descriptor and Non Descriptor Systems

Abstract

Multisensor distributed information fusion state estimator is presented in this paper. The algorithm deals with descriptor and non descriptor discrete time time-invariant stochastic linear system which is described by state space model. Under the assumption of the observability of the system, the state of the system is a linear combination of input white noise, observation white noise and observation signal. Further non-recursive state estimators algorithm is presented, which can be computed by the white noise estimators and measurement predictor. In order to improve the accuracy of the state estimator, this paper presents information fusion algorithm including matrix weighted, diagonal matrices weighted, scalar weighted and covariance intersection fusion, in the sense of linear minimum variance. The formula of optimal weighting coefficients is given. The algorithm analyzes the relationship between the accuracy and the computation of the four fusion algorithm. A simulation example for non descriptor system with 3 sensors shows its correctness and effectiveness.