An equivalence-class approach to multiple-hypothesis tracking

This paper introduces an equivalence-class approach to multi-target tracking. The approach seeks to address a fundamental limitation in multiple-hypothesis tracking: its selection (albeit with some delay and after reasoning over multiple hypotheses) of a unique global hypothesis. For some problems, the resulting tracking solution does a poor job with respect to metrics of interest. We seek instead to identify a class of similar hypotheses that have a larger aggregate likelihood than the maximum likelihood solution and, more importantly, whose members provide an improved tracking solution. Correspondingly, we introduce the Equivalence-Class MHT (ECMHT) and show its performance benefits in two-target tracking scenarios with a network of synchronous sensors.
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