Covariance intersection fusion steady-state Kalman estimator for discrete time stochastic linear systems with system errors and sensor errors is presented in this paper. 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. The information fusion algorithm presented in this paper is covariance intersection fusion. Compared with the single sensor case, the accuracy of the fused filter is greatly improved. A simulation example of the target tracking controllable system with two sensors shows its effectiveness and correctness.