Ambiguous data association and entangled attribute estimation
David J. Trawick, Philip C. Du Toit, Randy Clinton Paffenroth, Gregory J. Norgard · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2012
This paper presents an approach to attribute estimation incorporating data association ambiguity. In modern tracking systems, time pressures often leave all but the most likely data association alternatives unexplored, possibly producing track inaccuracies. Numerica's Bayesian Network Tracking Database, a key part of its Tracker Adjunct Processor, captures and manages the data association ambiguity for further analysis and possible ambiguity reduction/resolution using subsequent data. Attributes are non-kinematic discrete sample space sensor data. They may be as distinctive as aircraft ID, or as broad as friend or foe. Attribute data may provide improvements to data association by a process known as Attribute Aided Tracking (AAT). Indeed, certain uniquely identifying attributes (e.g. aircraft ID), when continually reported, can be used to define data association (tracks are the collections of observations with the same ID). However, attribute data arriving infrequently, combined with erroneous choices from ambiguous data associations, can produce incorrect attribute and kinematic state estimation. Ambiguous data associations define the tracks that are entangled with each other. Attribute data observed on an entangled track then modify the attribute estimates on all tracks entangled with it. For example, if a red track and a blue track pass through a region of data association ambiguity, these tracks become entangled. Later red observations on one entangled track make the other track more blue, and reduce the data association ambiguity. Methods for this analysis have been derived and implemented for efficient forward filtering and forensic analysis.