  We introduce the first unified theory for target tracking using Multiple
Hypothesis Tracking, Topological Data Analysis, and machine learning. Our
string of innovations are 1) robust topological features are used to encode
behavioral information, 2) statistical models are fitted to distributions over
these topological features, and 3) the target type classification methods of
Wigren and Bar Shalom et al. are employed to exploit the resulting likelihoods
for topological features inside of the tracking procedure. To demonstrate the
efficacy of our approach, we test our procedure on synthetic vehicular data
generated by the Simulation of Urban Mobility package.
