In this chapter, the DKF problem is addressed by reducing it into a dynamic consensus problem in term of weighted average estimates matrix that can be viewed as data fusion problem. We have presented a Distributed Kalman Filter based on polynomial filter to accelerate the distributed average consensus in the static network topologies. The proposed algorithm performs closely to the central filter, and also reduces the filter complexity at each node by reducing the dimension of the data. Thus, it scales computational complexity. Being based on sending only the estimates between neighbors, it also reduced radically the communication requirements. The proposed DKF contributes to significant energy saving.