Toward a Theoretical Foundation for Distributed Fusion

This chapter proposes the elements of a general theoretical foundation for multisource-multitarget track-to-track fusion (T²F). Single-target T²F when the track sources are dependent because of known double-counting. The chapter is based on the Bayesian theoretical foundation for single-target tracking, the single-target Bayes nonlinear filter. Measurement-to-track fusion refers to the process of collecting measurement data and then using it to improve the accuracy of the most recent estimates of the numbers and states of targets. Single-target track data is the consequence of some recursive filtering process, such as an extended Kalman filter, and consequently is inherently time-correlated. Formulate an optimal solution to the problem at hand—typically in the form of some kind of multisource-multitarget recursive Bayes filter. A single target is being tracked and that s independent sources, relying on their own dedicated local sensors, provide track data about this target to a T²F site.