Tracking solutions vary based on their speed and the assumptions that they make about measurements and target motion.
Among the fastest methods are Kalman Filters. These reduce tracking to a linear algebra problem by making many assumptions. They are optimal when the assumptions are true, but suffer from inaccuracies and model errors when the assumptions are not met.
In contrast, Particle Filters are slower, but involve far fewer assumptions. These provide high quality solutions over a wider set of cases, but they also require more powerful computers. They have gained popularity as computer speeds have increased and are heavily used by Metron in many innovative ways to solve a variety of challenging tracking problems.