A Bayesian network is a tree structure where each branch represents a classification candidate. The leaves of the tree
represent observable target features such as frequency or length. An optimized tree groups similar features together, e.g.
frequency and pulse width, while collecting dissimilar or disparate information, e.g. spectral and kinematics, all within
the same unifying structure. A vehicular track then is a subset of the a priori candidate library and contains only feasible
branches. The algorithm for updating the confidence of each feasible candidate according to Bayes' rule is embedded in
each track, as is the ability of a track to learn, apply a priori probability distributions, switch modes, switch among
kinematics models, apply tracking history to classification and apply classification history to tracking, and support multisensor
correlation and sensor fusion.