PMHT with the True Association Probability

Abstract

The probabilistic multi-hypothesis tracker (PMHT) offers a balance between the single-frame approach of the probabilistic data association filter (PDAF) and the multiple frame approach of the multiple hypothesis tracker (MHT). With single-frame tracking algorithms, only information that has been received to date is used to determine the association between tracks and measurements. These decisions are made based on available data and are not changed even when future data may indicate that the decision was incorrect. On the other hand, in multi-frame algorithms, hard decisions are delayed until some time in the future, thus allowing the possibility that incorrect association decisions may be corrected with more data. This paper presents the ongoing results of research using the PMHT algorithm as a network-level composite tracker on distributed platforms. In particular, this paper discusses and explores different approaches to calculating the association probabilities within the PMHT algorithm. The results are presented for multiple targets with just a single sensor at this point in the research.