Optimal/robust distributed data fusion: a unified approach

In past presentations, in the book Mathematics of Data Fusion, and in the recent monograph An Introduction to Multisource-Multitarget Statistics and Its Applications, we have shown how Finite-Set Statistics" (FISST) provides a unified foundation for the following aspects of multisource-multitarget data fusion: detection, identification, tracking, multi-evidence accrual, sensor management, performance estimation, and decision-making. In this paper we apply FISST to the distributed fusion problem: i.e., fusing the outputs produced by geographically separated data fusion systems. We propose two different approaches: optimal (assuming that correlations are completely known) and robust (assuming that correlations are completely unknown). Optimal distributed fusion is achieved via a direct FISST multitarget generalization of the Chong-Mori-Chang single-target track-fusion technique. Robust distributed fusion is achieved by using FISST to generalize the Uhlmann-Julier Covariance Intersection (CI) method to the multitarget case.