The concept of consensus filters for sensor fusion is not an entirely new proposition but one with an internally implemented Bayesian fusion is. This work documents a novel state update algorithm for sensor fusion which works using the principle of Bayesian fusion of data with variance implemented on a single integrator consensus algorithm. Comparative demonstrations of how consensus over a pinning network is reached are presented along with a weighted Bayesian Luenberger type observer and a ’Consensus on estimates’ algorithm. This type of a filter is something that is novel and has not been encountered in previous literature related to this topic to the best of our knowledge. In this work, we also extend the proof for a distributed Luenberger type observer design to include the case where the network being considered is a strongly connected digraph.