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Most modern maximum likelihood multiple target tracking systems (e.g., Multiple Hypothesis Tracking (MHT) and Numerica's
Multiple Frame Assignment (MFA)) need to determine how to separate their input measurements into subsets
corresponding to the observations of individual targets. These observation sets form the tracks of the system, and the
process of determining these sets is known as data association. Real-time constraints frequently force the use of only the
maximum likelihood choice for data association (over some time window), although alternative data association choices
may have been considered in the process of choosing the most likely.
This paper presents a Tracker Adjunct Processing (TAP) system that captures and manages the uncertainty encountered
in making data association decisions. The TAP combines input observation data and the data association alternatives
considered by the tracker into a dynamic Bayesian network (DBN). The network efficiently represents the combined
alternative tracking hypotheses. Bayesian network evidence propagation methods are used to update the network in light of
new evidence, which may consist of new observations, new alternative data associations, newly received late observations,
hypothetical connections, or other flexible queries. The maximum likelihood tracking hypothesis can then be redetermined,
which may result in changes to the best tracking hypothesis. The recommended changes can then be communicated back
to the associated tracking system, which can then update its tracks. In this manner, the TAP's interpretation makes the firm,
fixed (formerly maximum likelihood) decisions of the tracker "softer," i.e., less absolute. The TAP can also assess (and
reassess) track purity regions by ambiguity level.
We illustrate the working of the TAP with several examples, one in particular showing the incorporation of critical, late
or infrequent data. These data are critical in the sense that they are very valuable in resolving ambiguities in tracking and
combat identification; thus, the motivation to use these data is high even though there are complexities in applying it. Some
data may be late because of significant network delays, while other data may be infrequently reported because they come
from "specialized" sensors that provide updates only every once in a while.
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