Optimal track fusion using Bayes factors

In deciding whether to associate and fuse a group of tracks sent from independent local trackers, use should be made of all the data supporting them during their common history. This paper provides a closed-form expression for the relevant Bayes factor, which is the ratio of the probability that the tracks are caused by a single target to the probability that they are each caused by a different target. The expression is recursive, and it applies to processes which may include a linear Gaussian process and several discrete Markov processes. Two variants are provided, one that requires the data to be sent from local trackers to the fusion centre, and one that only requires discrete probabilities state estimates and covariance matrices