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, Adel I. El-Fallah, Raman K. Mehra, John R. Hoffman, Ronald P. S. Mahler, 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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