The problem of the state estimation of partially unknown, linear systems with non-Gaussian initial conditions in a multisensor environment is addressed in this paper. Two distributed algorithms are presented which can locally process the data collected by different local sensor subsystems. The local estimates are forwarded to a central processing center where the overall optimal estimate is obtained. The proposed algorithms are computationally attractive as well as theoretically interesting