  For distributed estimations in a sensor network, the consistency and accuracy
of an estimator are greatly affected by the unknown correlations between
individual estimates. An inconsistent or too conservative estimate may degrade
the estimation performance and even cause divergence of the estimator.
Cooperative estimation methods based on Inverse Covariance Intersection (ICI)
can utilize a network of sensors to provide a consistent and tight estimate of
a target. In this paper, unlike most existing ICI-based estimators that only
consider two-dimensional (2-D) target state estimation in the vector space, we
address this problem in a 3-D environment by extending the ICI algorithm to the
augmented quaternion space. In addition, the proposed algorithm is fully
distributed, as each agent only uses the local information from itself and its
communication neighbors, which is also robust to a time-varying communication
topology. To evaluate the performance, we test the proposed algorithm in a
camera network to track the pose of a target. Extensive Monte Carlo simulations
have been performed to show the effectiveness of our approach.
