earticle

영어

In this paper, a multisensor distributed information fusion state estimator for discrete time stochastic linear systems with random sensor errors is presented. Based on state-space model, the white noise estimator and the observation predictor are applied in this algorithm. Modern time series analysis method and Gevers-Wouters(G-W) algorithm are also used in this paper. The algorithm can deal with the filtering, smoothing and prediction problems via a unified method. In order to improve the estimation accuracy, the multisensor distributed information fusion method is adopted, which calculates the weighting parameters with the forms of matrix, diagonal matrices and scalars respectively, in the sense of linear minimum variance. Among those three kinds of fusion methods, the method weighted by matrix has the highest accuracy but more computation, while the one weighted by scalar has the lowest accuracy but less computation. A simulation example for a typical tracking system with 3-sensor shows the correctness, validity and no obvious difference among three kinds of the fusion algorithms.