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Proceedings Paper

Fuzzy multicriteria decision making in the assignment problem
Author(s): Elana Dror-Rein; Harvey B. Mitchell
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Paper Abstract

Many multi-target tracking systems work by continually updating the target tracks on the basis of received target measurements. This involves solving the assignment problem, i.e. finding the maximum set of track-to-measurement associations with the largest overall likelihood. To minimize the number of association errors, it is traditional to include targets for which there are no corresponding tracks and tracks for which there are no corresponding measurements. These missing tracks and missing measurements are taken into account by augmenting the track-to-measurement likelihood matrix. In this paper, we present a new approach to solving the assignment problem which does not involve augmenting the likelihood matrix. The solution found in the new approach contains n* high-quality track-to-measurement associations. This set of n* associations optimally satisfies two criteria: (1) a high average likelihood and (2) n* close to the expected number of true track-to-measurement associations. Thus, the number of track-to- measurement associations, n*, is not specified beforehand but rather is an output of the algorithm. The two criteria are defined by fuzzy membership functions, and the solution is found using a fuzzy multi-criteria decision-making algorithm.

Paper Details

Date Published: 29 October 1997
PDF: 10 pages
Proc. SPIE 3163, Signal and Data Processing of Small Targets 1997, (29 October 1997); doi: 10.1117/12.279535
Show Author Affiliations
Elana Dror-Rein, ELTA Electronics Industries Ltd. (Israel)
Harvey B. Mitchell, ELTA Electronics Industries Ltd. (Israel)

Published in SPIE Proceedings Vol. 3163:
Signal and Data Processing of Small Targets 1997
Oliver E. Drummond, Editor(s)

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