Practical multi-hypothesis trackers (MHTs) often entail a number of parameters for track confirmation and extraction logic, gating and pruning, most of which are chosen heuristically to tradeoff performance and computational cost. Conceptually, these parameters are unnecessary with optimal MHT processing, as these decisions will fall out from the optimal solution, though perhaps with an increase in processing cost. We demonstrate, however, with a canonical MHT model and its attendant association assignment problem that many of these parameters can be chosen losslessly, that is, they only remove hypotheses that an optimal association solution is guaranteed to remove anyway, and thus strictly improve computational cost, with no loss in tracking performance. At the other end of the spectrum, the tools developed likewise yield a number of relations that detect when parameters are set such that practical tracking is no longer possible.

Multi-sensor multi-target tracking requires the solution to a challenging data association problem. The problem simplifies when a portion of the target state vector and the corresponding sensor data satisfy a particular Markovian assumption. This leads to quantifiable benefits in performance vs. complexity of the tracking solution. This paper summarizes recently-obtained technical advances in graph-based tracking and applies this to a benchmark study with respect to an advanced track-oriented multiple-hypothesis tracking solution.