1. A multiple hypothesis tracking method of finding an optimal set within target track data objects, each particular target track data object based on collected sensor data and having a likelihood score and a list of target track data objects that are incompatible with that particular target track data object, the method comprising: by a hardware processor: initializing a plurality of target track partial hypothesis sets in a memory, each target track partial hypothesis set having one or more compatible target track data objects and an associated likelihood score; raisin the likelihood scores of the plurality of target track partial hypothesis sets by repeatedly adding multiple compatible target track data objects to the plurality of target track partial hypothesis sets, the compatible target track data objects tested in parallel in multiple expanded target track hypotheses by a plurality of processors by: (a) at each of the plurality of processors, generating a list of expanded target track hypotheses by loading a list of the target track partial hypothesis sets from a memory and testing pairs of target track data objects for compatibility for each target track partial hypothesis set, and (b) calculating a score for each expanded target track hypothesis in the generated list, each one of the plurality of processors calculating the score for a particular expanded target track hypothesis in the generated list in parallel; and selecting a group of the expanded target track hypotheses having a highest score.