Success Rates and Posterior Probabilities in Multiple Hypothesis Tracking
Edmund Brekke, Mandar Anil Chitre · 2018
In multiple hypothesis tracking (MHT) the outcome space is partitioned into a discrete collection of events known as association hypotheses, whose posterior probabilities are calculated. The discrete nature of this problem means that it can be viewed as a classification problem. In this paper we argue that the hypothesis probabilities must obey some bounds from classification theory. These bounds can be used to investigate the correctness of MHT implementations.