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Proceedings Paper

Optimal/robust distributed data fusion: a unified approach
Author(s): Ronald P. S. Mahler
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Paper Abstract

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.

Paper Details

Date Published: 4 August 2000
PDF: 11 pages
Proc. SPIE 4052, Signal Processing, Sensor Fusion, and Target Recognition IX, (4 August 2000); doi: 10.1117/12.395064
Show Author Affiliations
Ronald P. S. Mahler, Lockheed Martin Corp. (United States)


Published in SPIE Proceedings Vol. 4052:
Signal Processing, Sensor Fusion, and Target Recognition IX
Ivan Kadar, Editor(s)

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