In past presentations, in the book Mathematics of Data Fusion, and in the recent monograph An Introduction to Multisource-Mulitarget 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 geographical separated data fusion systems. We propose two different approaches: optimal and robust. Optimal distributed fusion is achieved via a direct FISST multitarget generalization of the Chong-Mori- Change single-target track-fusion technique. Robust distributed fusion is achieved by using FISST to generalize the Uhlmann-Julier Covariance Intersection method to the multitarget case.
[1]
R. E. Bethel,et al.
A PDF multitarget tracker
,
1994
.
[2]
Y. Ho,et al.
A Bayesian approach to problems in stochastic estimation and control
,
1964
.
[3]
Ronald Mahler,et al.
Multisource multitarget filtering: a unified approach
,
1998,
Defense, Security, and Sensing.
[4]
Yaakov Bar-Shalom,et al.
Estimation and Tracking: Principles, Techniques, and Software
,
1993
.
[5]
Michael I. Miller,et al.
Jump-diffusion processes for the automated understanding of FLIR scenes
,
1994,
Defense, Security, and Sensing.
[6]
Ronald P. S. Mahler,et al.
Global posterior densities for sensor management
,
1998,
Defense, Security, and Sensing.