Proceedings Article | 5 January 2004
KEYWORDS: Expectation maximization algorithms, Detection and tracking algorithms, Data modeling, Sensors, Target detection, Statistical modeling, Missiles, Error analysis, Infrared search and track, Algorithm development
Multiple frame data association, whether it is based on multiple
hypothesis tracking or multi-dimensional assignment problems, has
established itself as the method of choice for difficult tracking
problems, principally due to the ability to hold difficult data
association decisions in abeyance until additional information is
available. Over the last twenty years, these methods have focused
on one-to-one assignments, many-to-one, and many-to-many
assignments. Group tracking, on the other hand, introduces new
complexity into the association process, especially if some soft
decision making capability is desired. Thus, the goal of this
work is to combine multiple grouping hypotheses for each frame
of data (tracks or measurements) with matching these hypotheses
across multiple frames of data using one-to-one, many-to-one, or
many-to-many assignments to determine the correct hypothesis on
each frame of data and connectivity across the frames. The resulting formulation is sufficiently general to cover four broad classes of problems in multiple target tracking, namely (a) group cluster tracking, (b) pixel (clump) IR cluster tracking, (c) the merged measurement problem, and (d) MHT for track-to-track fusion.
What is more, the cluster assignment problem for either two or multiple dimensions represents a generalized data association problem in the sense that it reduces to the classical assignment problems when there are no overlapping groups or clusters. The formulation of the assignment problem for resolved object tracking and candidate group methods for use in multiple frame group tracking are briefly reviewed. Then, three different formulations of the group assignment problem are developed.
