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Proceedings Paper

Genetic optimization of the parameters of a track-while-detect algorithm
Author(s): David Montana
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Paper Abstract

We have developed an algorithm to detect the presence of narrowband signals and track the time evolution of their center frequencies. This algorithm has 35 parameters whose optimal values depend on (among other things): (1) the expected dynamics of the signals, (2) the background statistics, and (3) the clutter (i.e., the number of simultaneous signals). Manually optimizing these parameters is a difficult task not only because of the large number of parameters but also because of the interdependence of their effects on performance. We have therefore devised an automated method for optimizing the parameters. It has three basic components: (1) a 'truth' database with a graphical interface for easy manual entry of 'truth', (2) a scoring function which is a linear combination of six subscores (three evaluating detection performance and three evaluating tracking performance), and (3) a distributed genetic algorithm which optimizes the parameter values for a particular truth database. We have used this procedure to optimize the parameter values to a variety of signal types and environmental conditions. The results have been improved performance as well as the ability to make the algorithm adaptive: as the system detects changes in the environmental conditions, it can switch to a different set of parameters.

Paper Details

Date Published: 20 August 1992
PDF: 10 pages
Proc. SPIE 1706, Adaptive and Learning Systems, (20 August 1992); doi: 10.1117/12.139953
Show Author Affiliations
David Montana, Bolt, Beranek & Newman, Inc. (United States)

Published in SPIE Proceedings Vol. 1706:
Adaptive and Learning Systems
Firooz A. Sadjadi, Editor(s)

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