Decentralized systems merit a detailed analysis in view of the potential advantages that they offer. These include significant improvements in fault tolerance, modularity and scalability. Such attributes are required by a number of systems that are currently being planned within the defence and civil aerosense sectors. A recognized difficulty with the decentralized network architecture is the potential it creates for redundant data to proliferate as a result of cyclic information flows. This can lead to estimation biases and divergence. Solutions which require the network information sources to be tagged in some way are not generally possible without relaxing some of the constraints on which the decentralized paradigm is founded. This paper consequently investigates a different approach. Specifically, it examines the application of the Covariance Intersection (CI) data fusion technique. CI is relevant to the redundant data problem because it guarantees consistent estimates without requiring correlations to be maintained. The estimation performance of CI is compared here, with respect to a restricted Kalman approach, for a dynamic multi-platform network example. It is concluded that a hybrid CI/Kalman approach offers the best solution, since it exploits known independent information and unknown correlated information without having to relax the decentralized constraints.