25 August 2003 Initial studies on direct sensor management optimization using tracking performance metrics and genetic algorithms
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Abstract
In this paper we consider the problem of autonomously improving upon a sensor management algorithm for better tracking performance. Since various Performance Metrics have been proposed and studied for monitoring a tracking system's behavior, the problem is solvable by first parameterizing a sensor management algorithm and then searching the parameter space for a (sub-)optimal solution. Genetic Algorithms (GA) are ideally suited for this optimization task. In our GA approach, the sensor management algorithm is driven by "rules" that has a "condition" part to specify track locations and uncertainties, and an "action" part to specify where the Field of Views (FoVs) of the sensors should be directed. Initial simulation studies using a Multi-Hypothesis Tracker and the Kullback-Leibler metric (as a basis for the GA fitness function) are presented. They indicate that the method proposed is feasible and promising.
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Lingji Chen, Lingji Chen, Adel I. El-Fallah, Adel I. El-Fallah, Raman K. Mehra, Raman K. Mehra, John R. Hoffman, John R. Hoffman, Ronald P. S. Mahler, Ronald P. S. Mahler, Mark G. Alford, Mark G. Alford, } "Initial studies on direct sensor management optimization using tracking performance metrics and genetic algorithms", Proc. SPIE 5096, Signal Processing, Sensor Fusion, and Target Recognition XII, (25 August 2003); doi: 10.1117/12.487147; https://doi.org/10.1117/12.487147
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