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.

Date Published: 25 August 2003
PDF: 10 pages
Proc. SPIE 5096, Signal Processing, Sensor Fusion, and Target Recognition XII, (25 August 2003); doi: 10.1117/12.487147

Published in SPIE Proceedings Vol. 5096:
Signal Processing, Sensor Fusion, and Target Recognition XII
Ivan Kadar, Editor(s)