Generalized covariance intersection based on noise decomposition

In linear decentralized estimation, several nodes concurrently aim to estimate the state of a common phenomenon by means of local measurements and data exchanges. In this contribution, an efficient algorithm for consistent estimation of linear systems in sensor networks is derived. The main theorems generalize Covariance Intersection by means of an explicit consideration of individual noise terms. We apply the results to linear decentralized estimation and obtain covariance bounds with a scalable precision between the exact covariances and the bounds provided by Covariance Intersection.