Multi-sensor Information Fusion Steady-State Kalman Estimator for Systems with System Errors and Sensor Errors

Abstract

In this paper, a multi-sensor information fusion steady-state Kalman estimator for discrete time stochastic linear systems with system errors and sensor errors is presented. Gevers-Wouters(G-W) algorithm is used in this paper. Steady-state Kalman estimator is presented in this paper avoids the complex Diophantine equation, and it is based on the ARMA model to compute the steady-state Kalman estimators gain, further the Lyapunov equation is used to estimate the variance matrix and covariance matrix of estimation error. So this algorithm can obviously reduce the computational burden. In order to improve the estimation accuracy, the multi-sensor information fusion method is adopted. The fusion method includes weighted measurement fusion, weighted by scalars and the covariance intersection fusion. Under the linear minimum variance optimal information fusion criterion, the calculation formula of optimal weighting coefficients have be given in order to realize scalars weighted. To avoid the calculation of cross-covariance matrices, another distributed fusion filter is also presented by using the covariance intersection fusion algorithm, which can reduce the computational burden. And the relationship between the accuracy and the computation complexities among the three fusion algorithm are analyzed. A simulation example of the target tracking controllable system with two sensors shows its effectiveness and correctness.