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Proceedings Paper

Neural Network Implementations Of Data Association Algorithms For Sensor Fusion
Author(s): Donald E. Brown; Clarence L. Pittard; Worthy N. Martin
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Paper Abstract

The paper is concerned with locating a time varying set of entities in a fixed field when the entities are sensed at discrete time instances. At a given time instant a collection of bivariate Gaussian sensor reports is produced, and these reports estimate the location of a subset of the entities present in the field. A database of reports is maintained, which ideally should contain one report for each entity sensed. Whenever a collection of sensor reports is received, the database must be updated to reflect the new information. This updating requires association processing between the database reports and the new sensor reports to determine which pairs of sensor and database reports correspond to the same entity. Algorithms for performing this association processing are presented. Neural network implementation of the algorithms, along with simulation results comparing the approaches are provided.

Paper Details

Date Published: 14 September 1989
PDF: 10 pages
Proc. SPIE 1100, Sensor Fusion II, (14 September 1989); doi: 10.1117/12.960488
Show Author Affiliations
Donald E. Brown, University of Virginia (United States)
Clarence L. Pittard, University of Virginia (United States)
Worthy N. Martin, University of Virginia (United States)

Published in SPIE Proceedings Vol. 1100:
Sensor Fusion II
Charles B. Weaver, Editor(s)

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