Covariance intersection (CI) is an algorithm for combining two or more estimates of state variables in a Kalman filter when the correlation between them is unknown.
The use of a fixed measure is necessary for rigor to ensure that a sequence of updates does not cause the filtered covariance to increase.
[9] It is widely believed that unknown correlations exist in a diverse range of multi-sensor fusion problems.
To compensate this kind of divergence, a common sub-optimal approach is to artificially increase the system noise.
However, this heuristic requires considerable expertise and compromises the integrity of the Kalman filter framework.