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In this paper, a new extended Kalman particle filter based information fusion is proposed for state estimation problem of nonlinear and non-Gaussian systems. It uses extended Kalman filter algorithm to update particles in particle filter, with which the local state estimated values can be calculated. The multi-sensor information fusion filter is obtained by applying the standard linear minimum variance...
In this paper, an unscented particles filter based distributed information fusion is proposed for state estimation problem of nonlinear and non-Gaussian systems. It uses unscented Kalman filter algorithm to update particle; then calculates local state estimated values by particle filter. The system fusion estimation is obtained by applying the fusion rule weighted by scales. The simulation results...
By the modern time series analysis methods, based on ARMA innovation model and augmented state space model, a multisensor optimal information fusion Wiener deconvolution predictor weighted by scalars is proposed. The formulas of computing the local predictor error variances and cross-covariances are given, which are applied to compute optimal weighting coefficients. Compared to the single sensor case,...
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