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Proceedings Paper

Considerations for multiple hypothesis correlation on tactical platforms
Author(s): Alan M. Thomas; James E. Turpen
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Paper Abstract

Tactical platforms benefit greatly from the fusion of tracks from multiple sources in terms of increased situation awareness. As a necessary precursor to this track fusion, track-to-track association, or correlation, must first be performed. The related measurement-to-track fusion problem has been well studied with multiple hypothesis tracking and multiple frame assignment methods showing the most success. The track-to-track problem differs from this one in that measurements themselves are not available but rather track state update reports from the measuring sensors. Multiple hypothesis, multiple frame correlation systems have previously been considered; however, their practical implementation under the constraints imposed by tactical platforms is daunting. The situation is further exacerbated by the inconvenient nature of reports from legacy sensor systems on bandwidth- limited communications networks. In this paper, consideration is given to the special difficulties encountered when attempting the correlation of tracks from legacy sensors on tactical aircraft. Those difficulties include the following: covariance information from reporting sensors is frequently absent or incomplete; system latencies can create temporal uncertainty in data; and computational processing is severely limited by hardware and architecture. Moreover, consideration is given to practical solutions for dealing with these problems in a multiple hypothesis correlator.

Paper Details

Date Published: 29 May 2013
PDF: 8 pages
Proc. SPIE 8756, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2013, 875604 (29 May 2013); doi: 10.1117/12.2018066
Show Author Affiliations
Alan M. Thomas, Georgia Tech Research Institute (United States)
James E. Turpen, Georgia Tech Research Institute (United States)


Published in SPIE Proceedings Vol. 8756:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2013
Jerome J. Braun, Editor(s)

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