Home | english  | Impressum | Datenschutz | Sitemap | KIT
Convex Optimization Approaches to Long-Term Sensor Scheduling

Technischer Bericht


Marco F. Huber


Technischer Bericht IES-2009-01. In: Jürgen Beyerer, Marco Huber (Hrsg.), Proceedings of the 2009 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory, KIT Scientific Publishing, 2009.



The optimization over long time horizons in order to consider long-term effects is of paramount importance for effective sensor scheduling in multi-sensor systems like sensor arrays or sensor networks. Determining the optimal sensor schedule, however, is equivalent to solving a binary integer program, which is computationally demanding for long time horizons and many sensors. For linear Gaussian models, two efficient long-term sensor scheduling approaches are proposed in this report. The first approach determines approximate but close to optimal sensor schedules via convex optimization. The second approach combines convex optimization with a branch-and-bound search for efficiently determining the optimal sensor schedule. Both approaches are compared by means of numerical simulations.