Abstract. This paper is concerned with the design of distributed fusion filter for networked systems with unknown measurement interferences and packet dropouts. A Bernoulli distributed random variable is used to depict the phenomenon of packet dropouts. Without any prior information about the interference, a recursive Kalman-type state filter independent of the unknown interferences is designed for each sensor subsystem by applying the linear unbiased minimum variance estimation criterion. Based on the state filters of individual subsystems, the estimation error cross-covariance matrices between any two subsystems are derived. Then, the distributed fusion filter is designed by using the matrix-weighted fusion estimation algorithm in the linear minimum variance sense. Simulation results show the effectiveness of the proposed algorithms.