ABSTRACT A statistical approach to multiple target tracking is presented which allows for birth, death, splitting and merging of targets. Targets are also allowed to go undetected for several frames. The splitting and merging of targets is a novel addition for a statistically based tracking algorithm. This addition is essential for the tracking of storms, which is the motivation for this work. The utility of this tracker extends well beyond the tracking of storms however. It can be valuable in other tracking applications that have splitting or merging, such as vortexes, radar/sonar signals, or groups of people. The method assumes that the location of a target behaves like a Gaussian Process when it is observable. A Markov State Model decides when the birth, death, splitting, or merging of targets takes place. The tracking estimate is achieved by an algorithm that finds the tracks that maximize the conditional density of the unknown variables given the data. The problem of how to quantify the confidence in a tracking estimate is addressed as well. Some theoretical properties of this tracking estimates are also developed such as sufficient conditions for consistency. The properties of the proposed method will be demonstrated on simulated data. Finally, the method is applied to the problem for which it was designed, tracking storms from radar reflectivity data.
Advisory Committee: Thomas Lee and Jan Hannig (Co-Advisors), Jay Breidt, Doug Nychka, Aubrey Poore (Mathematics)