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Proceedings Paper

Comparison of robustized assignment algorithms
Author(s): Ivan Kadar; Eitan R. Eadan; Richard R. Gassner
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Paper Abstract

Several assignment methods are compared in terms of problem size, computational complexity and misassignment as a function of sparsity and gating. Specific real world applications include multi-target multi-sensor tracking/fusion and resource management with sparse cost matrices. The cost matrix computational complexity is also addressed. Both randomly generated cost matrices and measured data sets are used to test the algorithms. It is shown that, both standard and some new greedy, assignment algorithms significantly degrade in performance with fully gated columns and/or rows. However, it is shown that it is possible to modify specific algorithms to regain the lost optimality.

Paper Details

Date Published: 28 July 1997
PDF: 10 pages
Proc. SPIE 3068, Signal Processing, Sensor Fusion, and Target Recognition VI, (28 July 1997); doi: 10.1117/12.280802
Show Author Affiliations
Ivan Kadar, Northrop Grumman Corp. (United States)
Eitan R. Eadan, Northrop Grumman Corp. (United States)
Richard R. Gassner, Rome Lab. (United States)

Published in SPIE Proceedings Vol. 3068:
Signal Processing, Sensor Fusion, and Target Recognition VI
Ivan Kadar, Editor(s)

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