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Proceedings Paper

Fuzzy logic multisensor association algorithm: dealing with multiple targets, intermittent data, and noise
Author(s): James F. Smith
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Paper Abstract

A recursive multisensor association algorithm has been developed based on fuzzy logic. It simultaneously determines fuzzy grades of membership and fuzzy cluster centers. It is capable of associating data from various sensor types and in its simplest form makes no assumption about noise statistics as many association algorithms do. The algorithm is capable of performing without operator intervention. It associates data from the same target for multiple sensor types. The algorithm also provides an estimate of the number of targets present, reduced noise estimates of the quantities being measured, and a measure of confidence to assign to the data association. A comparison of the algorithm to a more conventional Bayesian association algorithm is provided. The data from both the ESM and radar systems is noisy and the ESM data is intermittent. The radar data has probability of detection less than unity. The effect of a large number of targets being present in the data on parameter estimation, determination of the number of targets and multisensor data association is examined. A method for determining sliding data window size based on fuzzy clustering for the multitarget case is discussed.

Paper Details

Date Published: 17 July 1998
PDF: 12 pages
Proc. SPIE 3374, Signal Processing, Sensor Fusion, and Target Recognition VII, (17 July 1998); doi: 10.1117/12.327090
Show Author Affiliations
James F. Smith, Naval Research Lab. (United States)

Published in SPIE Proceedings Vol. 3374:
Signal Processing, Sensor Fusion, and Target Recognition VII
Ivan Kadar, Editor(s)

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