Improved fast covariance intersection for distributed data fusion

Among the possible approaches to tackling the problem of data incest in distributed data fusion networks, covariance intersection is a candidate that yields consistent estimates independent of network structure and any possible cross-correlation between local estimates. The corresponding weighting coefficients are usually chosen with the aim for a minimum trace or determinant of the resulting error variance matrix. A fast non-iterative algorithm exists that, to a certain extent, approximately solves this nonlinear optimization problem with extremely reduced numerical implementation effort. Yet, the obtained weighting coefficients do not depend on the relative orientation of the estimation error variance matrices which may lead to a degraded performance in certain applications. Hence, an improved fast covariance intersection algorithm is developed that comes with a slightly increased implementation effort while yielding significantly better estimation results in some cases and comparable results in all other ones. Simulation results confirm the postulated performance improvement.